【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：

程序摘自【4】。

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：