plot_cv_predict.zip_cross_val_predict_plot(cv._predict-python_预测

""" ==================================== Plotting Cross-Validated Predictions ==================================== This example shows how to use `cross_val_predict` to visualize prediction errors. """ from sklearn import datasets from sklearn.cross_validation import cross_val_predict from sklearn import linear_model import matplotlib.pyplot as plt lr = linear_model.LinearRegression() boston = datasets.load_boston() y = boston.target # cross_val_predict returns an array of the same size as `y` where each entry # is a prediction obtained by cross validated: predicted = cross_val_predict(lr, boston.data, y, cv=10) fig, ax = plt.subplots() ax.scatter(y, predicted) ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4) ax.set_xlabel('Measured') ax.set_ylabel('Predicted') plt.show()