38 lines
1.5 KiB
Python
38 lines
1.5 KiB
Python
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from sklearn.linear_model import LinearRegression, Perceptron, Ridge
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.model_selection import cross_val_score
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from sklearn.pipeline import Pipeline
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def launch_linear_regression(x_train, x_test, y_train, y_test):
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my_linear_model = LinearRegression()
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my_linear_model.fit(x_train, y_train)
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linear_model_score = my_linear_model.score(
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x_test, y_test)
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print('linear_model_score: ', linear_model_score)
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return my_linear_model, linear_model_score
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# Perceptron
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def launch_perceptron(x_train, x_test, y_train, y_test):
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my_perceptron_model = Perceptron()
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my_perceptron_model.fit(x_train, y_train)
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perceptron_model_score = my_perceptron_model.score(
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x_test, y_test)
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print('perceptron_model_score: ', perceptron_model_score)
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return my_perceptron_model, perceptron_model_score
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# RidgePolyRegression
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def launch_ridge_poly_regression(x_train, x_test, y_train, y_test):
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my_polynomial_model = PolynomialFeatures(degree=3, include_bias=False)
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ridge = Ridge(alpha=1)
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pipeline = Pipeline(
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[("polynomial_features", my_polynomial_model), ("ridge_regression", ridge)])
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pipeline.fit(x_train, y_train)
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scores = cross_val_score(pipeline, x_test, y_test,
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scoring="neg_mean_squared_error", cv=5)
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polynomial_model_score = -scores.mean()
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print('mean polynomial_model_score: ', polynomial_model_score)
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return my_polynomial_model, polynomial_model_score
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