From 7d5800de03e7ade1ed6c8143ff3b6180b9d93e1b Mon Sep 17 00:00:00 2001 From: frog24 Date: Sun, 16 Feb 2025 21:09:14 +0400 Subject: [PATCH] =?UTF-8?q?=D1=85=D1=83=D0=B4=D1=88=D0=B0=D1=8F=20=D0=BC?= =?UTF-8?q?=D0=BE=D0=B4=D0=B5=D0=BB=D1=8C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lab_7/lab7.ipynb | 4452 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 4452 insertions(+) create mode 100644 lab_7/lab7.ipynb diff --git a/lab_7/lab7.ipynb b/lab_7/lab7.ipynb new file mode 100644 index 0000000..2c3fcc0 --- /dev/null +++ b/lab_7/lab7.ipynb @@ -0,0 +1,4452 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Импорт библиотек и загрузка данных" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2772\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import skfuzzy as fuzz\n", + "import matplotlib.pyplot as plt\n", + "from skfuzzy import control as ctrl\n", + "\n", + "df = pd.read_csv(\"../dataset.csv\")\n", + "print(df.shape[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Удаление выбросов и проверка на пустые значения(их нет)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "было 2772\n", + "age 39.10966810966811 14.081459420836477\n", + "bmi 30.70134920634921 6.1294486949652205\n", + "children 1.1026753434562546 1.2157555494600176\n", + "charges 13325.498588795157 12200.175109274192\n", + "стало 2710\n", + "age 0\n", + "sex 0\n", + "bmi 0\n", + "children 0\n", + "smoker 0\n", + "region 0\n", + "charges 0\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "print(\"было \", df.shape[0])\n", + "for column in df.select_dtypes(include=['int', 'float']).columns:\n", + " mean = df[column].mean()\n", + " std_dev = df[column].std()\n", + " print(column, mean, std_dev)\n", + " \n", + " lower_bound = mean - 3 * std_dev\n", + " upper_bound = mean + 3 * std_dev\n", + " \n", + " df = df[(df[column] <= upper_bound) & (df[column] >= lower_bound)]\n", + " \n", + "print(\"стало \", df.shape[0])\n", + "df = df.reset_index(drop=True)\n", + "\n", + "print(df.isnull().sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Выбор выходных и выходной переменной.\n", + "Мне кажется, что возраст и индекс массы тела будут наиболее сильно влиять на цену страховки" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "min age: 18 |min bmi: 15.96 |min charges: 1121.8739\n", + "max age: 64 |max bmi: 49.06 |max charges: 49577.6624\n" + ] + } + ], + "source": [ + "print('min age: ', df['age'].min(), '|min bmi: ', df['bmi'].min(), '|min charges: ', df['charges'].min())\n", + "print('max age: ', df['age'].max(), '|max bmi: ', df['bmi'].max(), '|max charges: ', df['charges'].max())\n", + "\n", + "age = np.arange(df['age'].min(), df['age'].max() + 1e-9, 1)\n", + "bmi = np.arange(df['bmi'].min(), df['bmi'].max() + 1e-9, 0.001)\n", + "charges = np.arange(df['charges'].min(), df['charges'].max() + 1e-9, 0.01)\n", + "\n", + "age_ctrl = ctrl.Antecedent(age, 'age')\n", + "bmi_ctrl = ctrl.Antecedent(bmi, 'bmi')\n", + "charges_ctrl = ctrl.Consequent(charges, 'charges')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Настройка параметров лигвистических переменных" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11801.75692885742\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\ulstu\\3.1\\mii\\AIM-PIbd-31-Barsukov-P-O\\aimenv\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", + " fig.show()\n", + "c:\\ulstu\\3.1\\mii\\AIM-PIbd-31-Barsukov-P-O\\aimenv\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", + " func(*args, **kwargs)\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\ulstu\\3.1\\mii\\AIM-PIbd-31-Barsukov-P-O\\aimenv\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", + " fig.canvas.print_figure(bytes_io, **kw)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "age_ctrl[\"young\"] = fuzz.zmf(age_ctrl.universe, 17, 31)\n", + "age_ctrl[\"middle-aged\"] = fuzz.trapmf(age_ctrl.universe, [29, 35, 45, 55])\n", + "age_ctrl[\"old\"] = fuzz.smf(age_ctrl.universe, 50, 65)\n", + "age_ctrl.view()\n", + "\n", + "bmi_ctrl[\"low bmi\"] = fuzz.zmf(bmi_ctrl.universe, 15, 17.5)\n", + "bmi_ctrl[\"normal bmi\"] = fuzz.trapmf(bmi_ctrl.universe, [17, 18.5, 25, 30])\n", + "bmi_ctrl[\"high bmi\"] = fuzz.smf(bmi_ctrl.universe, 27, 50)\n", + "bmi_ctrl.view()\n", + "\n", + "charge_min = df['charges'].min()\n", + "charge_max = df['charges'].max()\n", + "charge_mean = df['charges'].mean()\n", + "cheap_end = charge_min + (charge_mean - charge_min) * 0.5\n", + "expensive_start = charge_mean + (charge_max - charge_mean) * 0.5\n", + "\n", + "std = df['charges'].std()\n", + "print(std)\n", + "\n", + "charges_ctrl[\"cheap\"] = fuzz.zmf(charges_ctrl.universe, charge_min, cheap_end + std)\n", + "charges_ctrl[\"normal\"] = fuzz.trimf(charges_ctrl.universe, [cheap_end, charge_mean, expensive_start])\n", + "charges_ctrl[\"expensive\"] = fuzz.smf(charges_ctrl.universe, expensive_start - std, charge_max)\n", + "charges_ctrl.view()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Формировка базы нечетких правил" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rule1 = ctrl.Rule(age_ctrl[\"young\"] & bmi_ctrl[\"low bmi\"], charges_ctrl[\"normal\"])\n", + "rule2 = ctrl.Rule(age_ctrl[\"young\"] & bmi_ctrl[\"normal bmi\"], charges_ctrl[\"cheap\"])\n", + "rule3 = ctrl.Rule(age_ctrl[\"young\"] & bmi_ctrl[\"high bmi\"], charges_ctrl[\"expensive\"])\n", + "rule4 = ctrl.Rule(age_ctrl[\"middle-aged\"] & bmi_ctrl[\"low bmi\"], charges_ctrl[\"normal\"])\n", + "rule5 = ctrl.Rule(age_ctrl[\"middle-aged\"] & bmi_ctrl[\"normal bmi\"], charges_ctrl[\"cheap\"])\n", + "rule6 = ctrl.Rule(age_ctrl[\"middle-aged\"] & bmi_ctrl[\"high bmi\"], charges_ctrl[\"expensive\"])\n", + "rule7 = ctrl.Rule(age_ctrl[\"old\"] & bmi_ctrl[\"low bmi\"], charges_ctrl[\"expensive\"])\n", + "rule8 = ctrl.Rule(age_ctrl[\"old\"] & bmi_ctrl[\"normal bmi\"], charges_ctrl[\"normal\"])\n", + "rule9 = ctrl.Rule(age_ctrl[\"old\"] & bmi_ctrl[\"high bmi\"], charges_ctrl[\"expensive\"])\n", + "\n", + "rule1.view()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "charges_ctrl = ctrl.ControlSystem(\n", + " [\n", + " rule1,\n", + " rule2,\n", + " rule3,\n", + " rule4,\n", + " rule5,\n", + " rule6,\n", + " rule7,\n", + " rule8,\n", + " rule9,\n", + " ]\n", + ")\n", + "\n", + "charges = ctrl.ControlSystemSimulation(charges_ctrl)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Оценка качества" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 18\n", + " - young : 0.9897959183673469\n", + " - middle-aged : 0.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 34.1\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.1905860113421551\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9897959183673469\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9897959183673469\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9897959183673469\n", + " - bmi[high bmi] : 0.1905860113421551\n", + " age[young] AND bmi[high bmi] = 0.1905860113421551\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.1905860113421551\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.1905860113421551\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.1905860113421551\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 37547.62782934363\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.1905860113421551\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 30\n", + " - young : 0.01020408163265306\n", + " - middle-aged : 0.16666666666666666\n", + " - old : 0.0\n", + "Antecedent: bmi = 35.3\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.26045368620037795\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.01020408163265306\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.01020408163265306\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.01020408163265306\n", + " - bmi[high bmi] : 0.26045368620037795\n", + " age[young] AND bmi[high bmi] = 0.01020408163265306\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.01020408163265306\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.16666666666666666\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.16666666666666666\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.16666666666666666\n", + " - bmi[high bmi] : 0.26045368620037795\n", + " age[middle-aged] AND bmi[high bmi] = 0.16666666666666666\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.16666666666666666\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.26045368620037795\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 37361.15326835597\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.16666666666666666\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 57\n", + " - young : 0.0\n", + " - middle-aged : 0.0\n", + " - old : 0.4355555555555556\n", + "Antecedent: bmi = 43.7\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.8499432892249529\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.8499432892249529\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.8499432892249529\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.4355555555555556\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.4355555555555556\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.4355555555555556\n", + " - bmi[high bmi] : 0.8499432892249529\n", + " age[old] AND bmi[high bmi] = 0.4355555555555556\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.4355555555555556\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 38965.88917573576\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.4355555555555556\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 48\n", + " - young : 0.0\n", + " - middle-aged : 0.7\n", + " - old : 0.0\n", + "Antecedent: bmi = 30.78\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.054020415879017084\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.054020415879017084\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.7\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.7\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.7\n", + " - bmi[high bmi] : 0.054020415879017084\n", + " age[middle-aged] AND bmi[high bmi] = 0.054020415879017084\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.054020415879017084\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.054020415879017084\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 36176.92274912711\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.054020415879017084\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 44\n", + " - young : 0.0\n", + " - middle-aged : 1.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 39.52\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.5847621928166352\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.5847621928166352\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[high bmi] : 0.5847621928166352\n", + " age[middle-aged] AND bmi[high bmi] = 0.5847621928166352\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.5847621928166352\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.5847621928166352\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 39590.8934937229\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.5847621928166352\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 25\n", + " - young : 0.36734693877551017\n", + " - middle-aged : 0.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 24.985\n", + " - low bmi : 0.0\n", + " - normal bmi : 1.0\n", + " - high bmi : 0.0\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.36734693877551017\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.36734693877551017\n", + " - bmi[normal bmi] : 1.0\n", + " age[young] AND bmi[normal bmi] = 0.36734693877551017\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.36734693877551017\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.36734693877551017\n", + " - bmi[high bmi] : 0.0\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 1.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.0\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 1.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.0\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 7602.136966603081\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.36734693877551017\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.0\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 18\n", + " - young : 0.9897959183673469\n", + " - middle-aged : 0.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 38.665\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.5142448960302455\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9897959183673469\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9897959183673469\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9897959183673469\n", + " - bmi[high bmi] : 0.5142448960302455\n", + " age[young] AND bmi[high bmi] = 0.5142448960302455\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.5142448960302455\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.5142448960302455\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.5142448960302455\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 39310.188271770734\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.5142448960302455\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 57\n", + " - young : 0.0\n", + " - middle-aged : 0.0\n", + " - old : 0.4355555555555556\n", + "Antecedent: bmi = 31.16\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.06542759924385637\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.06542759924385637\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.06542759924385637\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.4355555555555556\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.4355555555555556\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.4355555555555556\n", + " - bmi[high bmi] : 0.06542759924385637\n", + " age[old] AND bmi[high bmi] = 0.06542759924385637\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.06542759924385637\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 36336.644540380206\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.06542759924385637\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 40\n", + " - young : 0.0\n", + " - middle-aged : 1.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 41.23\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.7092139886578448\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.7092139886578448\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[high bmi] : 0.7092139886578448\n", + " age[middle-aged] AND bmi[high bmi] = 0.7092139886578448\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.7092139886578448\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.7092139886578448\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 40040.382651007356\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.7092139886578448\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 26\n", + " - young : 0.25510204081632654\n", + " - middle-aged : 0.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 23.7\n", + " - low bmi : 0.0\n", + " - normal bmi : 1.0\n", + " - high bmi : 0.0\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.25510204081632654\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.25510204081632654\n", + " - bmi[normal bmi] : 1.0\n", + " age[young] AND bmi[normal bmi] = 0.25510204081632654\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.25510204081632654\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.25510204081632654\n", + " - bmi[high bmi] : 0.0\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 1.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.0\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 1.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.0\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 7981.3435369366025\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.25510204081632654\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.0\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 31\n", + " - young : 0.0\n", + " - middle-aged : 0.3333333333333333\n", + " - old : 0.0\n", + "Antecedent: bmi = 26.62\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.6759999999999998\n", + " - high bmi : 0.0\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.6759999999999998\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.0\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.3333333333333333\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.3333333333333333\n", + " - bmi[normal bmi] : 0.6759999999999998\n", + " age[middle-aged] AND bmi[normal bmi] = 0.3333333333333333\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.3333333333333333\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.3333333333333333\n", + " - bmi[high bmi] : 0.0\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.6759999999999998\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.0\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 7708.734623071976\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.3333333333333333\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.0\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 37\n", + " - young : 0.0\n", + " - middle-aged : 1.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 29.83\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.03400000000000034\n", + " - high bmi : 0.030279395085066156\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.03400000000000034\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.030279395085066156\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[normal bmi] : 0.03400000000000034\n", + " age[middle-aged] AND bmi[normal bmi] = 0.03400000000000034\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.03400000000000034\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[high bmi] : 0.030279395085066156\n", + " age[middle-aged] AND bmi[high bmi] = 0.030279395085066156\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.030279395085066156\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.03400000000000034\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.030279395085066156\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 25216.886501150875\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.03400000000000034\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.030279395085066156\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 18\n", + " - young : 0.9897959183673469\n", + " - middle-aged : 0.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 31.35\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.07154064272211727\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9897959183673469\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9897959183673469\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9897959183673469\n", + " - bmi[high bmi] : 0.07154064272211727\n", + " age[young] AND bmi[high bmi] = 0.07154064272211727\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.07154064272211727\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.07154064272211727\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.07154064272211727\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 36416.255778197825\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.07154064272211727\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 33\n", + " - young : 0.0\n", + " - middle-aged : 0.6666666666666666\n", + " - old : 0.0\n", + "Antecedent: bmi = 36.29\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.3262914933837429\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.3262914933837429\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.6666666666666666\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.6666666666666666\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.6666666666666666\n", + " - bmi[high bmi] : 0.3262914933837429\n", + " age[middle-aged] AND bmi[high bmi] = 0.3262914933837429\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.3262914933837429\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.3262914933837429\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 38414.960616727825\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.3262914933837429\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 48\n", + " - young : 0.0\n", + " - middle-aged : 0.7\n", + " - old : 0.0\n", + "Antecedent: bmi = 28.88\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.2240000000000002\n", + " - high bmi : 0.01336257088846882\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.2240000000000002\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.01336257088846882\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.7\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.7\n", + " - bmi[normal bmi] : 0.2240000000000002\n", + " age[middle-aged] AND bmi[normal bmi] = 0.2240000000000002\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.2240000000000002\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.7\n", + " - bmi[high bmi] : 0.01336257088846882\n", + " age[middle-aged] AND bmi[high bmi] = 0.01336257088846882\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.01336257088846882\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.2240000000000002\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.01336257088846882\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 11081.13376945518\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.2240000000000002\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.01336257088846882\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 19\n", + " - young : 0.9591836734693877\n", + " - middle-aged : 0.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 30.495\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.0461815689981097\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9591836734693877\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9591836734693877\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9591836734693877\n", + " - bmi[high bmi] : 0.0461815689981097\n", + " age[young] AND bmi[high bmi] = 0.0461815689981097\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0461815689981097\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.0461815689981097\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.0461815689981097\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 36056.70655871274\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.0461815689981097\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 23\n", + " - young : 0.6326530612244898\n", + " - middle-aged : 0.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 28.49\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.3020000000000003\n", + " - high bmi : 0.008393572778827987\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.6326530612244898\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.6326530612244898\n", + " - bmi[normal bmi] : 0.3020000000000003\n", + " age[young] AND bmi[normal bmi] = 0.3020000000000003\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.3020000000000003\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.6326530612244898\n", + " - bmi[high bmi] : 0.008393572778827987\n", + " age[young] AND bmi[high bmi] = 0.008393572778827987\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.008393572778827987\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.3020000000000003\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.008393572778827987\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.3020000000000003\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.008393572778827987\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 9378.106717931281\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.3020000000000003\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.008393572778827987\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 56\n", + " - young : 0.0\n", + " - middle-aged : 0.0\n", + " - old : 0.32000000000000006\n", + "Antecedent: bmi = 31.79\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.08674517958412098\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.08674517958412098\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.08674517958412098\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.32000000000000006\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.32000000000000006\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.32000000000000006\n", + " - bmi[high bmi] : 0.08674517958412098\n", + " age[old] AND bmi[high bmi] = 0.08674517958412098\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.08674517958412098\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 36599.95792495265\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.08674517958412098\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 41\n", + " - young : 0.0\n", + " - middle-aged : 1.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 32.2\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.10223062381852566\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.10223062381852566\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[high bmi] : 0.10223062381852566\n", + " age[middle-aged] AND bmi[high bmi] = 0.10223062381852566\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.10223062381852566\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.10223062381852566\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 36770.27476173566\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.10223062381852566\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 27\n", + " - young : 0.16326530612244897\n", + " - middle-aged : 0.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 20.045\n", + " - low bmi : 0.0\n", + " - normal bmi : 1.0\n", + " - high bmi : 0.0\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.16326530612244897\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.16326530612244897\n", + " - bmi[normal bmi] : 1.0\n", + " age[young] AND bmi[normal bmi] = 0.16326530612244897\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.16326530612244897\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.16326530612244897\n", + " - bmi[high bmi] : 0.0\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 1.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.0\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 1.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.0\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 8371.995208460137\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.16326530612244897\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.0\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 29\n", + " - young : 0.04081632653061224\n", + " - middle-aged : 0.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 26.03\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.7939999999999998\n", + " - high bmi : 0.0\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.04081632653061224\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.04081632653061224\n", + " - bmi[normal bmi] : 0.7939999999999998\n", + " age[young] AND bmi[normal bmi] = 0.04081632653061224\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.04081632653061224\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.04081632653061224\n", + " - bmi[high bmi] : 0.0\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.7939999999999998\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.0\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.7939999999999998\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.0\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 9180.405370648012\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.04081632653061224\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.0\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 61\n", + " - young : 0.0\n", + " - middle-aged : 0.0\n", + " - old : 0.8577777777777778\n", + "Antecedent: bmi = 44.0\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.8638941398865785\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.8638941398865785\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.8638941398865785\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.8577777777777778\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.8577777777777778\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.8577777777777778\n", + " - bmi[high bmi] : 0.8638941398865785\n", + " age[old] AND bmi[high bmi] = 0.8577777777777778\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.8577777777777778\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 40508.591013083904\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.8577777777777778\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 41\n", + " - young : 0.0\n", + " - middle-aged : 1.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 28.8\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.23999999999999985\n", + " - high bmi : 0.012249527410207978\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.23999999999999985\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.012249527410207978\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[normal bmi] : 0.23999999999999985\n", + " age[middle-aged] AND bmi[normal bmi] = 0.23999999999999985\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.23999999999999985\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 1.0\n", + " - bmi[high bmi] : 0.012249527410207978\n", + " age[middle-aged] AND bmi[high bmi] = 0.012249527410207978\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.012249527410207978\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.23999999999999985\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.012249527410207978\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 10660.585814446153\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.23999999999999985\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.012249527410207978\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 30\n", + " - young : 0.01020408163265306\n", + " - middle-aged : 0.16666666666666666\n", + " - old : 0.0\n", + "Antecedent: bmi = 31.57\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.07895992438563332\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.01020408163265306\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.01020408163265306\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.01020408163265306\n", + " - bmi[high bmi] : 0.07895992438563332\n", + " age[young] AND bmi[high bmi] = 0.01020408163265306\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.01020408163265306\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.16666666666666666\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.16666666666666666\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.16666666666666666\n", + " - bmi[high bmi] : 0.07895992438563332\n", + " age[middle-aged] AND bmi[high bmi] = 0.07895992438563332\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.07895992438563332\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.07895992438563332\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 36508.22379837373\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.07895992438563332\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 47\n", + " - young : 0.0\n", + " - middle-aged : 0.8\n", + " - old : 0.0\n", + "Antecedent: bmi = 36.08\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.3117066162570888\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.3117066162570888\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.8\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.8\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.8\n", + " - bmi[high bmi] : 0.3117066162570888\n", + " age[middle-aged] AND bmi[high bmi] = 0.3117066162570888\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.3117066162570888\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.3117066162570888\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 38333.32570189906\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.3117066162570888\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 50\n", + " - young : 0.0\n", + " - middle-aged : 0.5\n", + " - old : 0.0\n", + "Antecedent: bmi = 32.205\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.10242731568998108\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.10242731568998108\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.5\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.5\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.5\n", + " - bmi[high bmi] : 0.10242731568998108\n", + " age[middle-aged] AND bmi[high bmi] = 0.10242731568998108\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.10242731568998108\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.10242731568998108\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 36772.34652531745\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.10242731568998108\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 56\n", + " - young : 0.0\n", + " - middle-aged : 0.0\n", + " - old : 0.32000000000000006\n", + "Antecedent: bmi = 31.79\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.08674517958412098\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.0\n", + " - bmi[high bmi] : 0.08674517958412098\n", + " age[young] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.08674517958412098\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.32000000000000006\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.32000000000000006\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.32000000000000006\n", + " - bmi[high bmi] : 0.08674517958412098\n", + " age[old] AND bmi[high bmi] = 0.08674517958412098\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.08674517958412098\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 36599.95792495265\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.08674517958412098\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 20\n", + " - young : 0.9081632653061225\n", + " - middle-aged : 0.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 27.3\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.5399999999999998\n", + " - high bmi : 0.0003402646502835792\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9081632653061225\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9081632653061225\n", + " - bmi[normal bmi] : 0.5399999999999998\n", + " age[young] AND bmi[normal bmi] = 0.5399999999999998\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.5399999999999998\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9081632653061225\n", + " - bmi[high bmi] : 0.0003402646502835792\n", + " age[young] AND bmi[high bmi] = 0.0003402646502835792\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0003402646502835792\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.5399999999999998\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.0003402646502835792\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.5399999999999998\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.0003402646502835792\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 7183.657863624606\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.5399999999999998\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.0003402646502835792\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 23\n", + " - young : 0.6326530612244898\n", + " - middle-aged : 0.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 35.2\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.2542155009451798\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.6326530612244898\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.6326530612244898\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.6326530612244898\n", + " - bmi[high bmi] : 0.2542155009451798\n", + " age[young] AND bmi[high bmi] = 0.2542155009451798\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.2542155009451798\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.2542155009451798\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.2542155009451798\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 37987.54981555619\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.2542155009451798\n", + "\n", + "=============\n", + " Antecedents \n", + "=============\n", + "Antecedent: age = 19\n", + " - young : 0.9591836734693877\n", + " - middle-aged : 0.0\n", + " - old : 0.0\n", + "Antecedent: bmi = 30.59\n", + " - low bmi : 0.0\n", + " - normal bmi : 0.0\n", + " - high bmi : 0.04872627599243859\n", + "\n", + "=======\n", + " Rules \n", + "=======\n", + "RULE #0:\n", + " IF age[young] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9591836734693877\n", + " - bmi[low bmi] : 0.0\n", + " age[young] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #1:\n", + " IF age[young] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9591836734693877\n", + " - bmi[normal bmi] : 0.0\n", + " age[young] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #2:\n", + " IF age[young] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[young] : 0.9591836734693877\n", + " - bmi[high bmi] : 0.04872627599243859\n", + " age[young] AND bmi[high bmi] = 0.04872627599243859\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.04872627599243859\n", + "\n", + "RULE #3:\n", + " IF age[middle-aged] AND bmi[low bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[middle-aged] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #4:\n", + " IF age[middle-aged] AND bmi[normal bmi] THEN charges[cheap]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[middle-aged] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[cheap] : 0.0\n", + "\n", + "RULE #5:\n", + " IF age[middle-aged] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[middle-aged] : 0.0\n", + " - bmi[high bmi] : 0.04872627599243859\n", + " age[middle-aged] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #6:\n", + " IF age[old] AND bmi[low bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[low bmi] : 0.0\n", + " age[old] AND bmi[low bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "RULE #7:\n", + " IF age[old] AND bmi[normal bmi] THEN charges[normal]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[normal bmi] : 0.0\n", + " age[old] AND bmi[normal bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[normal] : 0.0\n", + "\n", + "RULE #8:\n", + " IF age[old] AND bmi[high bmi] THEN charges[expensive]\n", + "\tAND aggregation function : fmin\n", + "\tOR aggregation function : fmax\n", + "\n", + " Aggregation (IF-clause):\n", + " - age[old] : 0.0\n", + " - bmi[high bmi] : 0.04872627599243859\n", + " age[old] AND bmi[high bmi] = 0.0\n", + " Activation (THEN-clause):\n", + " charges[expensive] : 0.0\n", + "\n", + "\n", + "==============================\n", + " Intermediaries and Conquests \n", + "==============================\n", + "Consequent: charges = 36096.81833877887\n", + " cheap:\n", + " Accumulate using accumulation_max : 0.0\n", + " normal:\n", + " Accumulate using accumulation_max : 0.0\n", + " expensive:\n", + " Accumulate using accumulation_max : 0.04872627599243859\n", + "\n", + " age bmi charges Predicted\n", + "0 18 34.100 1137.01100 37547.627829\n", + "12 18 31.350 1622.18850 36416.255778\n", + "15 19 30.495 2128.43105 36056.706559\n", + "28 23 35.200 2416.95500 37987.549816\n", + "6 18 38.665 3393.35635 39310.188272\n", + "9 26 23.700 3484.33100 7981.343537\n", + "20 29 26.030 3736.46470 9180.405371\n", + "10 31 26.620 3757.84480 7708.734623\n", + "23 30 31.570 4837.58230 36508.223798\n", + "22 41 28.800 6282.23500 10660.585814\n", + "11 37 29.830 6406.41070 25216.886501\n", + "13 33 36.290 6551.75010 38414.960617\n", + "8 40 41.230 6610.10970 40040.382651\n", + "18 41 32.200 6875.96100 36770.274762\n", + "4 44 39.520 6948.70080 39590.893494\n", + "25 50 32.205 8835.26495 36772.346525\n", + "14 48 28.880 9249.49520 11081.133769\n", + "3 48 30.780 10141.13620 36176.922749\n", + "2 57 43.700 11576.13000 38965.889176\n", + "21 61 44.000 13063.88300 40508.591013\n", + "27 20 27.300 16232.84700 7183.657864\n", + "19 27 20.045 16420.49455 8371.995208\n", + "16 23 28.490 18328.23810 9378.106718\n", + "5 25 24.985 23241.47453 7602.136967\n", + "29 19 30.590 24059.68019 36096.818339\n", + "1 30 35.300 36837.46700 37361.153268\n", + "24 47 36.080 42211.13820 38333.325702\n", + "7 57 31.160 43578.93940 36336.644540\n", + "17 56 31.790 43813.86610 36599.957925\n", + "26 56 31.790 43813.86610 36599.957925\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test_df = df[['age', 'bmi', 'charges']].sample(30, random_state=13)\n", + "test_df = test_df.reset_index(drop=True)\n", + "\n", + "predicted = []\n", + "\n", + "for i in range(len(test_df)):\n", + " charges.input['age'] = test_df.loc[i, 'age']\n", + " charges.input['bmi'] = test_df.loc[i, 'bmi']\n", + " charges.compute()\n", + " a = charges.print_state()\n", + " predicted.append(charges.output['charges'])\n", + "\n", + "test_df['Predicted'] = predicted\n", + "test_df_sorted = test_df.sort_values(by='charges')\n", + "\n", + "print(test_df_sorted[['age', 'bmi', 'charges', 'Predicted']])\n", + "\n", + "# Визуализация\n", + "plt.figure(figsize=(10, 5))\n", + "plt.plot(test_df.index, test_df_sorted['charges'], marker='o', label='Charges', color='blue')\n", + "plt.plot(test_df.index, test_df_sorted['Predicted'], marker='s', label='Predicted', color='red')\n", + "plt.xlabel(\"Sample Index\")\n", + "plt.ylabel(\"Price\")\n", + "plt.legend()\n", + "plt.title(\"Сравнение реальных и предсказанных цен\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Как и ожидалось, модель ничего нормально не предсказала. Вероятно это из-за выбора неправильных параметров или недостаточноего числа параметров. Также вероятно база заданных правил неточная, потому что лингвистическая переменная возраста формировалась исключистельно субъективно, а переменная стоимости страхования на основе минимума, максимума, среднего и среднеквадратичного отклонения." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "aimenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} -- 2.25.1