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pb.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', 1000): + print(df[:5]) + +def prepareStringData(columnName): + uniq = df[columnName].unique() + mp = {} + for i in uniq: + mp[i] = len(mp) + df[columnName] = df[columnName].map(mp) + + +print() +print("Данные после подготовки:") +prepareStringData("gender") +prepareStringData("race/ethnicity") +prepareStringData("parental level of education") +prepareStringData("lunch") +prepareStringData("test preparation course") +with pb.option_context('display.max_rows', None, 'display.max_columns', None, 'display.width', 1000): + print(df[:5]) + +X = df[["gender", "race/ethnicity", "lunch", "test preparation course", "math score", "reading score", "writing score"]] +y = df["parental level of education"] +X_train, X_Test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=42) +dtc = DecisionTreeClassifier() +dtc = dtc.fit(X_train, y_train) +dtr = DecisionTreeRegressor() +dtr = dtr.fit(X_train, y_train) +print() +print("Результат дерева класификации на учебных данных: ", dtc.score(X_train, y_train)) +print("Результат дерева класификации на тестовых данных: ", dtc.score(X_Test, y_test)) +print() +print("Результат дерева регрессии на учебных данных: ", dtr.score(X_train, y_train)) +print("Результат дерева регрессии на тестовых данных: ", dtr.score(X_Test, y_test)) diff --git a/madyshev_egor_lab_3/readme.md b/madyshev_egor_lab_3/readme.md new file mode 100644 index 0000000..0e83aa5 --- /dev/null +++ b/madyshev_egor_lab_3/readme.md @@ -0,0 +1,41 @@ +# Задание +Решите с помощью библиотечной реализации дерева решений задачу из лабораторной работы «Веб-сервис «Дерево решений» по предмету «Методы искусственного интеллекта» на 99% ваших данных. Проверьте работу модели на оставшемся проценте, сделайте вывод +## Задание по варианту +Задача для дерева решений. Предсказание уровня образования родителей по всем остальных данным. +## Решение +### Запуск программы +Для запуска программы необходимо запустить файл main.py, содержащий код программы +### Используемые технологии +Программа использует следующие библиотеки: +- numpy - библиотека для работы с массивами и матрицами. +- matplotlib - библиотека для создания графиков и визуализации данных. +- sklearn - библиотека для машинного обучения и анализа данных. +### Что делает программа +Программа читает данные из csv файла. Подготавливает их для работы модели, приводя текстовые параметры к числам. И пытается научиться предсказывать уровень образования родителей по данным об их детях. +### Тесты +Данные без подготовки: + gender race/ethnicity parental level of education lunch test preparation course math score reading score writing score +0 female group B bachelor's degree standard none 72 72 74 +1 female group C some college standard completed 69 90 88 +2 female group B master's degree standard none 90 95 93 +3 male group A associate's degree free/reduced none 47 57 44 +4 male group C some college standard none 76 78 75 + +Данные после подготовки: + gender race/ethnicity parental level of education lunch test preparation course math score reading score writing score +0 0 0 0 0 0 72 72 74 +1 0 1 1 0 1 69 90 88 +2 0 0 2 0 0 90 95 93 +3 1 2 3 1 0 47 57 44 +4 1 1 1 0 0 76 78 75 + +Результат дерева классификации на учебных данных: 0.998989898989899 +Результат дерева классификации на тестовых данных: 0.2 + +Результат дерева регрессии на учебных данных: 0.9984005221729634 +Результат дерева регрессии на тестовых данных: -1.2264150943396226 + +По результатам двух типов моделей деревьев видно, что модель дерева решений не подходит для предсказания уровня образования родителей по этим данным. Или на практике не существует такой зависимости. +С целью проверки не переобучилась ли модель были проведены тесты с изменением параметров программы. +Изменение объема обучающей выборки и регулирование параметров моделей деревьев результат не поменяло. +Это значит, что модель не была переобучена. \ No newline at end of file