1. from sklearn.metrics import mean_square_error
    3. def plot_learning_curve(algo,X_train,X_test,y_train,y_test):  # algo的意思是学习器
    4.     train_score = []
    5.     test_score = []
    6.     for i in range(1, len(X_train)+1):
    7.         algo.fit(X_train[:i],y_train[:i])
    9.         y_train_predict = alog.predict(X_train[:i])
    10.         train_score.append(mean_square_error(y_train[:i], y_train_predict))
    12.         y_test_predict = alog.predict(X_test)
    13.         test_score.append(mean_square_error(y_test, y_test_predict))
    16.     plt.plot([i for i in range(1,len(X_train)+1)], np.sqrt(train_score),label='train')
    17.     plt.plot([i for i in range(1,len(X_test)+1)], np.sqrt(test_score),label='train')
    18.     plt.legend()  # 添加标签需要
    19.     plt.axis([0, len(X_train)+1,0,4])  # 绘制范围，前面俩个是横轴，后面俩个是纵轴
    20.     plt.show()
    22.     plot_learning_curve(Linearregression(), X_train,X_test,y_train,y_test)