【机器学习系列】scikit-learn中的Linear Regression Example

说明：

这里为了以后方面查阅浏览，只搬运了别人的基本代码，相关细节可查看其它资料。例如：

http://scikit-learn.org/0.11/auto_examples/linear_model/plot_ols.html

代码：

print __doc__# Code source: Jaques Grobler# License: BSDimport pylab as plimport numpy as npfrom sklearn import datasets, linear_model# Load the diabetes datasetdiabetes = datasets.load_diabetes()# Use only one featurediabetes_X = diabetes.data[:, np.newaxis]diabetes_X_temp = diabetes_X[:, :, 2]# Split the data into training/testing setsdiabetes_X_train = diabetes_X_temp[:-20]diabetes_X_test = diabetes_X_temp[-20:]from sklearn.datasets.samples_generator import make_regression# this is our test set, it's just a straight line with some# gaussian noiseX, Y = make_regression(n_samples=100, n_features=1, n_informative=1,\                        random_state=0, noise=35)# Split the targets into training/testing setsdiabetes_y_train = diabetes.target[:-20]diabetes_y_test = diabetes.target[-20:]# Create linear regression objectregr = linear_model.LinearRegression()# Train the model using the training setsregr.fit(diabetes_X_train, diabetes_y_train)# The coefficientsprint 'Coefficients: \n', regr.coef_# The mean square errorprint ("Residual sum of squares: %.2f" %        np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))# Explained variance score: 1 is perfect predictionprint ('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))# Plot outputspl.scatter(diabetes_X_test, diabetes_y_test,  color='black')pl.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue',        linewidth=3)pl.xticks(())pl.yticks(())pl.show()

效果图：