64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
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"""
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=========================================================
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Linear Regression Example
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=========================================================
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The example below uses only the first feature of the `diabetes` dataset,
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in order to illustrate the data points within the two-dimensional plot.
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The straight line can be seen in the plot, showing how linear regression
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attempts to draw a straight line that will best minimize the
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residual sum of squares between the observed responses in the dataset,
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and the responses predicted by the linear approximation.
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The coefficients, residual sum of squares and the coefficient of
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determination are also calculated.
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"""
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# Code source: Jaques Grobler
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn import datasets, linear_model
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from sklearn.metrics import mean_squared_error, r2_score
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# Load the diabetes dataset
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diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)
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# Use only one feature
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diabetes_X = diabetes_X[:, np.newaxis, 2]
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# Split the data into training/testing sets
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diabetes_X_train = diabetes_X[:-20]
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diabetes_X_test = diabetes_X[-20:]
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# Split the targets into training/testing sets
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diabetes_y_train = diabetes_y[:-20]
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diabetes_y_test = diabetes_y[-20:]
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# Create linear regression object
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regr = linear_model.LinearRegression()
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# Train the model using the training sets
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regr.fit(diabetes_X_train, diabetes_y_train)
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# Make predictions using the testing set
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diabetes_y_pred = regr.predict(diabetes_X_test)
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# The coefficients
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print("Coefficients: \n", regr.coef_)
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# The mean squared error
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print("Mean squared error: %.2f" % mean_squared_error(diabetes_y_test, diabetes_y_pred))
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# The coefficient of determination: 1 is perfect prediction
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print("Coefficient of determination: %.2f" % r2_score(diabetes_y_test, diabetes_y_pred))
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# Plot outputs
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plt.scatter(diabetes_X_test, diabetes_y_test, color="black")
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plt.plot(diabetes_X_test, diabetes_y_pred, color="blue", linewidth=3)
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plt.xticks(())
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plt.yticks(())
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plt.show()
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