Linear Regression Example
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代码主要来自:http://scikit-learn.org/stable/
误差函数: 采用最小二平方
代码如下:
print(__doc__) import matplotlib.pyplot as plt import numpy as np from sklearn import datasets, linear_model from sklearn.metrics import mean_squared_error, r2_score diabetes = datasets.load_diabetes() 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()
执行结果如下:
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