Scikit-learn：模型评估Model evaluation 之绘图

http://blog.csdn.net/pipisorry/article/details/53001866

绘制ROC曲线

def plotRUC(yt, ys, title=None):    '''    绘制ROC-AUC曲线    :param yt: y真值    :param ys: y预测值    '''    from sklearn import metrics    from matplotlib import pyplot as plt    f_pos, t_pos, thresh = metrics.roc_curve(yt, ys)    auc_area = metrics.auc(f_pos, t_pos)    plt.plot(f_pos, t_pos, 'darkorange', lw=2, label='AUC = %.2f' % auc_area)    plt.legend(loc='lower right')    plt.plot([0, 1], [0, 1], color='navy', linestyle='--')    plt.title('ROC-AUC curve for %s' % title)    plt.ylabel('True Pos Rate')    plt.xlabel('False Pos Rate')    plt.show()

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[python sklearn画ROC曲线 ]

皮皮blog

from: http://blog.csdn.net/pipisorry/article/details/53001866

ref: