python画出混淆矩阵

以下是Python代码，用于绘制混淆矩阵：

import matplotlib.pyplot as plt
import numpy as np

def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.tight_layout()

# 示例：
# 混淆矩阵数据
cm = np.array([[10, 2, 3],
               [1, 9, 2],
               [3, 2, 10]])

# 类别标签
classes = ['Class A', 'Class B', 'Class C']

# 绘制混淆矩阵
plot_confusion_matrix(cm, classes)

运行以上代码，将得到一个混淆矩阵的可视化结果。可以通过调整`cm`和`classes`参数来适应你的实际数据。