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- print(__doc__)
- # Code source: Jaques Grobler
- # License: BSD 3 clause
- import matplotlib.pyplot as plt
- import numpy as np
- from sklearn import datasets, linear_model
- from sklearn.metrics import mean_squared_error, r2_score
- # Load the diabetes dataset
- diabetes = datasets.load_diabetes()
- # Use only one feature
- diabetes_X = diabetes.data[:, np.newaxis, 2]
- # Split the data into training/testing sets
- diabetes_X_train = diabetes_X[:-20]
- diabetes_X_test = diabetes_X[-20:]
- # Split the targets into training/testing sets
- diabetes_y_train = diabetes.target[:-20]
- diabetes_y_test = diabetes.target[-20:]
- # Create linear regression object
- regr = linear_model.LinearRegression()
- # Train the model using the training sets
- regr.fit(diabetes_X_train, diabetes_y_train)
- # Make predictions using the testing set
- diabetes_y_pred = regr.predict(diabetes_X_test)
- # The coefficients
- print('Coefficients: \n', regr.coef_)
- # The mean squared error
- print("Mean squared error: %.2f"
- % mean_squared_error(diabetes_y_test, diabetes_y_pred))
- # Explained variance score: 1 is perfect prediction
- print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))
- # Plot outputs
- plt.scatter(diabetes_X_test, diabetes_y_test, color='black')
- plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)
- plt.xticks(())
- plt.yticks(())
- plt.show()
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