学习曲线函数

    from sklearn.svm import LinearSVC
    from sklearn.learning_curve import learning_curve
    #绘制学习曲线，以确定模型的状况
    def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
                            train_sizes=np.linspace(.1, 1.0, 5)):
        """
        画出data在某模型上的learning curve.
        参数解释
        ----------
        estimator : 你用的分类器。
        title : 表格的标题。
        X : 输入的feature，numpy类型
        y : 输入的target vector
        ylim : tuple格式的(ymin, ymax), 设定图像中纵坐标的最低点和最高点
        cv : 做cross-validation的时候，数据分成的份数，其中一份作为cv集，其余n-1份作为training(默认为3份)
        """
        plt.figure()
        train_sizes, train_scores, test_scores = learning_curve(
            estimator, X, y, cv=5, n_jobs=1, 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.fill_between(train_sizes, train_scores_mean - train_scores_std,
                         train_scores_mean + train_scores_std, alpha=0.1,
                         color="r")
        plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
                         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="Cross-validation score")
        plt.xlabel("Training examples")
        plt.ylabel("Score")
        plt.legend(loc="best")
        plt.grid("on") 
        if ylim:
            plt.ylim(ylim)
        plt.title(title)
        plt.show()
    #少样本的情况情况下绘出学习曲线
    plot_learning_curve(LinearSVC(C=10.0), "LinearSVC(C=10.0)",
                        X, y, ylim=(0.8, 1.01),
                        train_sizes=np.linspace(.05, 0.2, 5))