From 09b9bfc730bebd185f9a102e3e31f540dc26cb53 Mon Sep 17 00:00:00 2001 From: Mamoru777 Date: Mon, 30 Oct 2023 21:10:46 +0400 Subject: [PATCH] senkin_alexander_lab_2 is ready --- senkin_alexander_lab_2/.gitignore | 1 + senkin_alexander_lab_2/README.md | 49 +++++++++++++++++ senkin_alexander_lab_2/img.png | Bin 0 -> 95386 bytes .../senkin_alexander_lab_2.py | 50 ++++++++++++++++++ 4 files changed, 100 insertions(+) create mode 100644 senkin_alexander_lab_2/.gitignore create mode 100644 senkin_alexander_lab_2/README.md create mode 100644 senkin_alexander_lab_2/img.png create mode 100644 senkin_alexander_lab_2/senkin_alexander_lab_2.py diff --git a/senkin_alexander_lab_2/.gitignore b/senkin_alexander_lab_2/.gitignore new file mode 100644 index 0000000..e9aa8fb --- /dev/null +++ b/senkin_alexander_lab_2/.gitignore @@ -0,0 +1 @@ + .idea/ \ No newline at end of file diff --git a/senkin_alexander_lab_2/README.md b/senkin_alexander_lab_2/README.md new file mode 100644 index 0000000..a7a24c4 --- /dev/null +++ b/senkin_alexander_lab_2/README.md @@ -0,0 +1,49 @@ +Лабораторная №2 + +Вариант №2 + +Задание на лабораторную: + +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) + +Как запустить лабораторную работу: + +Чтобы увидеть работу программы, нужно запустить исполняемый питон файл senkin_alexander_lab_2.py, после чего в консоли будут выведены все признаки, их ранжирование и топ 4 признака по среднему значению значимости. + +Библиотеки + +Numpy. иблиотека для работы с массивами и матрицами чисел. Она используется для создания и манипуляции данными. + +Sklearn. Предоставляет инструменты и алгоритмы, которые упрощают задачи, связанные с машинным обучением. + +Описание программы: + +- Генерируем набор данных из 100 точек данных используя функцию make_circles +- С помощью функции train_test_split разделяем данные на тестовые и обучающие в соотношении 20 к 80 +- Добавляем дополнительные признаки, чтобы в сумме было 20 признаков +- Создаем 3 модели: +- Линейную регрессию (LinearRegression) +- Рекурсивное сокращение признаков (Recursive Feature Elimination – RFE) +- Сокращение признаков Случайными деревьями (Random Forest Regressor) +- Обучаем модели и производим ранжирование +- Линейная регрессия дает ранжирование признаков на основе абсолютных коэффициентов. +- RFE (Рекурсивное сокращение признаков) ранжирует признаки на основе их значимости для модели. +- Random Forest Regressor оценивает важность признаков на основе их вклада в прогнозы модели. +- Посредством вычисления среднего ранжирования по всем трем методам мы определяем, какие признаки являются самыми важными с учетом всех трех методов. + +![img.png](img.png) + +Делаем вывод, что по среднему значению самыми важными признаками являются 3, 17, 19 и 20 признаки \ No newline at end of file diff --git a/senkin_alexander_lab_2/img.png b/senkin_alexander_lab_2/img.png new file mode 100644 index 0000000000000000000000000000000000000000..bec9f5ae6ab3684698345ade8be820cc4a1154ce GIT binary patch literal 95386 zcmceecT|&Yy01k*KtvE!L`p!KpcF-V2~|L(NJp9i(tB?qf=H3xq&Go&M_MRRdPjN( zkxu9VLI@!zpx-y&?3q1hW}m&zf3CO)i}!h-a$ooLdxBMzWJ#{xy?W`=B@%f#Y1K=Y zaBVJKx-3a}8T-i$ZEg3ZOK&g9OG~J`8*HX}s8RHwj=**%?S3lsY?oh3N_Ugp=5Qn< z!_!&1N$(eQpYUGL+dvumh5PlJ8Be9Z`ii?&yd_AZJTftj=I%NW?eY&c>@P1A1N*0? zZKoC1?=+b%V>%Ve?_9g`?%ONwJE7WV5$XQt|2qG`9}gw>`#+vDy#fUP{yZ3Olkw5- zpNM)3V*2%?*I3BQB19uey*D%)Lx*>D|_DPvH zFq4LCP{a+dPKrJjF_qEDADvcCCD?hgnwNAxU0`NiE9)73XKTfctOX}C7E4&EKmD*S z)E(7hTeFsHK$(3Sk<281;2iUe!0)-Owt&9ni+z51{HSb;G0^Oz`ra#+CoagUgt965 z)>|Yh+Yw=AB#cW*tj`5mTKd%y=)(ABc2+_;{`=R-OeD9gU!A}IPDBYo+yv^ 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