可以计算准确率（Accuracy）， 精确率（Precision）， 召回率（Recall）和F1-Measure的代码

以下是Python的示例代码，用于计算准确率、精确率、召回率和F1-Measure的值：

# 实际标签和预测标签的数组
y_true = [1, 0, 1, 1, 0, 1, 1, 0, 1, 0]
y_pred = [1, 1, 1, 0, 0, 1, 0, 1, 0, 0]

# 计算TP、TN、FP、FN
TP = sum([1 for i in range(len(y_true)) if y_true[i] == 1 and y_pred[i] == 1])
TN = sum([1 for i in range(len(y_true)) if y_true[i] == 0 and y_pred[i] == 0])
FP = sum([1 for i in range(len(y_true)) if y_true[i] == 0 and y_pred[i] == 1])
FN = sum([1 for i in range(len(y_true)) if y_true[i] == 1 and y_pred[i] == 0])

# 计算准确率
accuracy = (TP + TN) / (TP + TN + FP + FN)
print("Accuracy:", accuracy)

# 计算精确率
precision = TP / (TP + FP)
print("Precision:", precision)

# 计算召回率
recall = TP / (TP + FN)
print("Recall:", recall)

# 计算F1-Measure
f1_measure = 2 * precision * recall / (precision + recall)
print("F1-Measure:", f1_measure)

在上面的示例代码中，`y_true`和`y_pred`分别表示实际标签和预测标签的数组。通过计算TP、TN、FP、FN来计算准确率、精确率、召回率和F1-Measure的值。最后，将计算的结果打印出来。