MCC 小结

MCC

1. 定义

MCC ——Matthews correlation coefficient , 马修斯相关系数。sklearn.metrics.matthews_corrcoef

The Matthews correlation coefficient is used in machine learning as a measure of the quality of binary and multiclass classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The MCC is in essence a correlation coefficient value between -1 and +1. A coefficient of +1 represents a perfect prediction, 0 an average random prediction and -1 an inverse prediction. The statistic is also known as the phi coefficient. [source: Wikipedia]

马修斯相关系数在机器学习中被用来衡量二分类和多分类。它考虑真和假阳性和阴性，通常被认为是一种平衡的测量，即使类别的数目非常不同，也可以使用。其本质是一个介于-1到+1之间的相关系数。

• 1代表完美预测
• 0代表平均随机预测
• -1代表相反的预测

也叫phi 系数。

2. 计算公式

MCC可以直接用混淆矩阵计算 MCC WiKi：

至于TP，FP， TN， FN跟TPR, FPR中计算是一样的

Matthew原本给出的公式：

3. 用法

用法：matthews_corrcoef(y_true, y_pred)

还有个参数 ：样本权重sample_weight：array-like of shape (n_samples,), default=None
返回float 常数

from sklearn.metrics import matthews_corrcoef

y_true = [+1, +1, +1, -1]
y_pred = [+1, -1, +1, +1]
matthews_corrcoef(y_true, y_pred)
======================================
-0.3333333333333333