哪種方法最適合計算無關數據集的馬修斯相關係數（MCC）值？

Question

哪種方法最適合計算無關數據集的馬修斯相關係數（MCC）值？

Answer 1

我不確定這裏的「最佳方法」是什麼意思，但給定confusion matrix，計算應該很簡單。在Python：

import math 

# tp is true positives, fn is false negatives, etc 
mcc = (tp*tn - fp*fn)/math.sqrt((tp + fp)*(tp + fn)*(tn + fp)*(tn + fn)) 

Answer 2

以前的答案是正確的，但是在公式中你可能想也考慮這些情況下，任何分母四個總和爲零;在這種情況下，分母可以任意設定爲1。

爲了完整起見，我下面將R代碼裏面（原代碼可以發現here）

mcc <- function (actual, predicted) 
{ 
    # handles zero denominator and verflow error on large-ish products in denominator. 
    # 
    # actual = vector of true outcomes, 1 = Positive, 0 = Negative 
    # predicted = vector of predicted outcomes, 1 = Positive, 0 = Negative 
    # function returns MCC 

    TP <- sum(actual == 1 & predicted == 1) 
    TN <- sum(actual == 0 & predicted == 0) 
    FP <- sum(actual == 0 & predicted == 1) 
    FN <- sum(actual == 1 & predicted == 0) 

    sum1 <- TP+FP; sum2 <-TP+FN ; sum3 <-TN+FP ; sum4 <- TN+FN; 
    denom <- as.double(sum1)*sum2*sum3*sum4 # as.double to avoid overflow error on large products 

    if (any(sum1==0, sum2==0, sum3==0, sum4==0)) { 
    denom <- 1 
    } 

    mcc <- ((TP*TN)-(FP*FN))/sqrt(denom) 
    return(mcc) 
}