python混淆矩阵代码

python
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np

# 实际标签
actual_labels = [0, 1, 0, 1, 0, 1, 0, 0, 1, 1]
# 预测标签
predicted_labels = [0, 1, 0, 1, 0, 0, 1, 0, 1, 1]

# 生成混淆矩阵
cm = confusion_matrix(actual_labels, predicted_labels)

# 可视化混淆矩阵
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
plt.title('Confusion Matrix')
plt.colorbar()
classes = ['Class 0', 'Class 1']
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes)
plt.yticks(tick_marks, classes)

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

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

这段代码使用了 sklearn.metrics 中的 confusion_matrix 函数来计算混淆矩阵，然后使用 matplotlib 来可视化混淆矩阵。

如果您想更详细地解释混淆矩阵，可以添加一些代码来计算准确率、召回率和 F1 分数。

python
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
import matplotlib.pyplot as plt
import numpy as np

# 实际标签
actual_labels = [0, 1, 0, 1, 0, 1, 0, 0, 1, 1]
# 预测标签
predicted_labels = [0, 1, 0, 1, 0, 0, 1, 0, 1, 1]

# 生成混淆矩阵
cm = confusion_matrix(actual_labels, predicted_labels)

# 计算准确率
accuracy = accuracy_score(actual_labels, predicted_labels)
print("Accuracy:", accuracy)

# 计算精确度
precision = precision_score(actual_labels, predicted_labels)
print("Precision:", precision)

# 计算召回率
recall = recall_score(actual_labels, predicted_labels)
print("Recall:", recall)

# 计算 F1 分数
f1 = f1_score(actual_labels, predicted_labels)
print("F1 Score:", f1)

# 可视化混淆矩阵
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
plt.title('Confusion Matrix')
plt.colorbar()
classes = ['Class 0', 'Class 1']
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes)
plt.yticks(tick_marks, classes)

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

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

这段代码添加了准确率、精确度、召回率和 F1 分数的计算，并将结果打印出来。