sklearn不同数量的训练集在测试集上的表现的曲线刻画

def plot_learning_curve(estimator,X,y,cv=5,train_sizes=[0.1,0.3,0.5,0.7,0.8,0.9]):
    """
    描述：对于不同数量的训练样本的估计器的验证和训练评分
    param estimator:object|
    param X:shape=[n_samples,n_feature]
    param y:shape=[n_samples,]
    param cv:int
    param train_size:list of float
    """
    import matplotlib.pyplot as plt
　　 from sklearn.model_selection import learning_curve

    plt.figure()
    plt.title("learning curves")
    plt.xlabel("Training examples")
    plt.ylabel("Score")

    train_sizes, train_scores, test_scores = learning_curve(estimator=estimator,
                                                            X=X,
                                                            y=y,
                                                            cv=cv,
                                                            train_sizes=train_sizes)
    train_scores_mean = np.mean(train_scores,axis=1)
    train_scores_std  = np.std(train_scores,axis=1)
    test_scores_mean  = np.mean(test_scores, axis=1)
    test_scores_std   = np.std(test_scores, axis=1)

    plt.grid()
    plt.fill_between(train_sizes,
                     y1=train_scores_mean-train_scores_std,
                     y2=train_scores_mean+train_scores_std,
                     alpha=0.1,
                     color="r")

    plt.fill_between(train_sizes,
                     y1=test_scores_mean-test_scores_std,
                     y2=test_scores_mean+test_scores_std,
                     alpha=0.1,
                     color="g")

    plt.plot(train_sizes,train_scores_mean,"o-",color="r",label="training score")
    plt.plot(train_sizes, test_scores_mean,'o-',color="g",label="testing score")

    plt.legend(loc="best")

    plt.show()

plot_learning_curve(estimator=SVC(),X=X,y=y,cv=5,train_sizes=[0.1,0.3,0.5,0.7,0.8,0.9])