From 39f0867f3ca6523aec906cfbd76d754cfbbb8dd4 Mon Sep 17 00:00:00 2001 From: BulatReznik <90419704+BulatReznik@users.noreply.github.com> Date: Sun, 8 Oct 2023 18:37:01 +0400 Subject: [PATCH] Add lab2 --- almukhammetov_bulat_lab_2/README.md | 40 ++++++++++++++ almukhammetov_bulat_lab_2/image-1.png | Bin 0 -> 21555 bytes almukhammetov_bulat_lab_2/image-2.png | Bin 0 -> 22517 bytes almukhammetov_bulat_lab_2/image-3.png | Bin 0 -> 10028 bytes almukhammetov_bulat_lab_2/image-4.png | Bin 0 -> 6938 bytes almukhammetov_bulat_lab_2/image.png | Bin 0 -> 9906 bytes almukhammetov_bulat_lab_2/lab2.py | 75 ++++++++++++++++++++++++++ 7 files changed, 115 insertions(+) create mode 100644 almukhammetov_bulat_lab_2/README.md create mode 100644 almukhammetov_bulat_lab_2/image-1.png create mode 100644 almukhammetov_bulat_lab_2/image-2.png create mode 100644 almukhammetov_bulat_lab_2/image-3.png create mode 100644 almukhammetov_bulat_lab_2/image-4.png create mode 100644 almukhammetov_bulat_lab_2/image.png create mode 100644 almukhammetov_bulat_lab_2/lab2.py diff --git a/almukhammetov_bulat_lab_2/README.md b/almukhammetov_bulat_lab_2/README.md new file mode 100644 index 0000000..74fb7ee --- /dev/null +++ b/almukhammetov_bulat_lab_2/README.md @@ -0,0 +1,40 @@ +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). + +Данные: +Линейная регрессия (LinearRegression) +Рекурсивное сокращение признаков (Recursive Feature Elimination –RFE) +Сокращение признаков Случайными деревьями (Random Forest Regressor) + +Запуск: +Запустите файл lab2.py + +Описание программы: +1. Генерирует случайные данные для задачи регрессии с помощью функции make_regression, создавая матрицу признаков X и вектор целевой переменной y. +2. Создает DataFrame data, в котором столбцы представляют признаки, а последний столбец - целевую переменную. +3. Разделяет данные на матрицу признаков X и вектор целевой переменной y. +4. Создает список моделей для ранжирования признаков: линейной регрессии, рекурсивного сокращения признаков и сокращения признаков случайными деревьями. +5. Создает словарь model_scores для хранения оценок каждой модели. +6. Обучает и оценивает каждую модель на данных: +7. Вычисляет ранги признаков и нормализует их в диапазоне от 0 до 1. +8. Выводит оценки признаков каждой модели и их средние оценки. +9. Находит четыре наиболее важных признака по средней оценке и выводит их индексы и значения. + +Результаты: + +![Alt text](image.png) + +![Alt text](image-1.png) + +![Alt text](image-2.png) + +![Alt text](image-3.png) + +![Alt text](image-4.png) + +Выводы: + +Четыре наиболее важных признака, определенных на основе средних оценок, включают Признак 6, Признак 1, Признак 2 и Признак 5. 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