commit 2
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Ctrl-Tim
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@ -4,10 +4,9 @@
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@ -132,13 +152,19 @@
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@ -9,12 +9,11 @@
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import numpy as np
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from sklearn.datasets import make_classification
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from sklearn.linear_model import LinearRegression, Perceptron, Ridge
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from matplotlib import pyplot as plt
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from matplotlib.colors import ListedColormap
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_absolute_error, mean_squared_error, accuracy_score, balanced_accuracy_score
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import PolynomialFeatures
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from matplotlib import pyplot as plt
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from matplotlib.colors import ListedColormap
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@ -48,10 +47,7 @@ def my_linear_regression(X_train, X_test, y_train, y_test):
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# вывод в консоль
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print()
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print('===> Линейная регрессия <===')
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print('Оценка точности:')
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print('MAE:', mean_absolute_error(y_test, y_pred))
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print('MSE:', mean_squared_error(y_test, y_pred))
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print()
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print('Оценка точности: ', lin_reg_model.score(X_train, y_train))
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# вывод в график
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plt.title('Линейная регрессия')
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@ -71,10 +67,7 @@ def my_perceptron(X_train, X_test, y_train, y_test):
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# вывод в консоль
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print()
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print('===> Персептрон <===')
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print('Оценка точности:')
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print('По тренировочным данным: ', perceptron_model.score(X_train, y_train))
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print('По тестовым данным: ', accuracy_score(y_test, y_pred))
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print()
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print('Оценка точности: ', perceptron_model.score(X_train, y_train))
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# вывод в график
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plt.title('Персептрон')
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@ -94,10 +87,7 @@ def my_poly_ridge(X_train, X_test, y_train, y_test):
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# вывод в консоль
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print()
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print('===> Гребневая полиномиальная регрессия <===')
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print('Оценка точности:')
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print('MAE:', mean_absolute_error(y_test, y_pred))
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print('MSE:', mean_squared_error(y_test, y_pred))
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print()
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print('Оценка точности: ', poly_rige_model.score(X_train, y_train))
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# вывод в график
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plt.title('Гребневая полиномиальная регрессия')
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