From 7eda43247197904927e1fc8b9674b9e4308d0efa Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=90=D1=80=D0=B7=D0=B0=D0=BC=D0=B0=D1=81=D0=BA=D0=B8?= =?UTF-8?q?=D0=BD=D0=B0=20=D0=9C=D0=B8=D0=BB=D0=B0=D0=BD=D0=B0?= Date: Mon, 20 Nov 2023 16:21:41 +0400 Subject: [PATCH] arzamaskina_milana_lab_2 is ready --- arzamaskina_milana_lab_2/README.md | 50 +++++++++++++ arzamaskina_milana_lab_2/img_result_1.png | Bin 0 -> 151929 bytes arzamaskina_milana_lab_2/img_result_2.png | Bin 0 -> 149501 bytes arzamaskina_milana_lab_2/img_result_3.png | Bin 0 -> 158772 bytes arzamaskina_milana_lab_2/img_result_4.png | Bin 0 -> 67366 bytes arzamaskina_milana_lab_2/main.py | 84 ++++++++++++++++++++++ 6 files changed, 134 insertions(+) create mode 100644 arzamaskina_milana_lab_2/README.md create mode 100644 arzamaskina_milana_lab_2/img_result_1.png create mode 100644 arzamaskina_milana_lab_2/img_result_2.png create mode 100644 arzamaskina_milana_lab_2/img_result_3.png create mode 100644 arzamaskina_milana_lab_2/img_result_4.png create mode 100644 arzamaskina_milana_lab_2/main.py diff --git a/arzamaskina_milana_lab_2/README.md b/arzamaskina_milana_lab_2/README.md new file mode 100644 index 0000000..3539c06 --- /dev/null +++ b/arzamaskina_milana_lab_2/README.md @@ -0,0 +1,50 @@ +## Лабораторная работа №2 + +### Ранжирование признаков + +Вариант №2 + +## Задание: + +Используя код из [1] (пункт «Решение задачи ранжирования признаков», стр. 205), +выполните ранжирование признаков с помощью указанных по варианту моделей. +Отобразите получившиеся значения\оценки каждого признака каждым методом\моделью и среднюю оценку. +Проведите анализ получившихся результатов. +Какие четыре признака оказались самыми важными по среднему значению? +(Названия\индексы признаков и будут ответом на задание). + +Модели: ++ Линейная регрессия (LinearRegression) ++ Рекурсивное сокращение признаков (Recursive Feature Elimination –RFE), ++ Сокращение признаков Случайными деревьями (Random Forest Regressor) + +### Какие технологии использовались: + +Используемые библиотеки: +* numpy +* pandas +* sklearn + +### Как запустить: + +* установить python, numpy, pandas, sklearn +* запустить проект (стартовая точка - main.py) + +### Что делает программа: + +* Генерирует данные и обучает модели: LinearRegression, Recursive Feature Elimination (RFE), Random Forest Regressor +* Производится ранжирование признаков с помощью моделей +* Отображение получившихся результатов: значения признаков для каждой модели и 4 самых важных признака по среднему значению + +### 4 самых важных признака по среднему значению +* Признак №1 : 0.887 +* Признак №4 : 0.821 +* Признак №2 : 0.741 +* Признак №11 : 0.600 + +#### Результаты работы программы: + +![Result1](img_result_1.png) +![Result2](img_result_2.png) +![Result3](img_result_3.png) +![Result4](img_result_4.png) \ No newline at end of file diff --git a/arzamaskina_milana_lab_2/img_result_1.png b/arzamaskina_milana_lab_2/img_result_1.png new file mode 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