python confusion matrix 混淆矩阵

【1】混淆矩阵（Confusion Matrix）概念

【2】 混淆矩阵-百度百科

【3】 Python中生成并绘制混淆矩阵（confusion matrix）

【4】 使用python绘制混淆矩阵（confusion_matrix）

 

示例：

Python画混淆矩阵程序示例，摘自【4】。

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from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt import numpy as np     def plot_confusion_matrix(cm, labels, title='Confusion Matrix'):     plt.imshow(cm, interpolation='nearest', cmap='Blues')     plt.title(title)     plt.colorbar()     xlocations = np.array(range(len(labels)))     plt.xticks(xlocations, labels, rotation=90)     plt.yticks(xlocations, labels)     plt.ylabel('True label')     plt.xlabel('Predicted label')     label = ["ant", "bird", "cat"] tick_marks = np.array(range(len(label))) + 0.5 y_true = [2, 0, 2, 2, 0, 1] y_pred = [0, 0, 2, 2, 0, 2]   cm = confusion_matrix(y_true, y_pred) np.set_printoptions(precision=2) cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print(cm_normalized) plt.figure(figsize=(12, 8), dpi=120)   ind_array = np.arange(len(label)) x, y = np.meshgrid(ind_array, ind_array)   for x_val, y_val in zip(x.flatten(), y.flatten()):     c = cm_normalized[y_val][x_val]     if c > 0.0:         plt.text(x_val, y_val, "%0.2f" % (c,), color='red', fontsize=17, va='center', ha='center')   # offset the tick plt.gca().set_xticks(tick_marks, minor=True) plt.gca().set_yticks(tick_marks, minor=True) plt.gca().xaxis.set_ticks_position('none') plt.gca().yaxis.set_ticks_position('none') plt.grid(True, which='minor', linestyle='-') plt.gcf().subplots_adjust(bottom=0.15)   plot_confusion_matrix(cm_normalized, label, title='Normalized confusion matrix') # plt.savefig('../Data/confusion_matrix.png', format='png') plt.show()

运行结果：