Python:计算 TP、FP、FN и TN

Question

我有具有真实类别和类别的数据框，这是通过某种算法预测的。

     true  pred
0       1     0
1       1     1
2       1     1
3       0     0
4       1     1

我尝试使用

def classification(y_actual, y_hat):
    TP = 0
    FP = 0
    TN = 0
    FN = 0

    for i in range(len(y_hat)): 
        if y_actual[i] == y_hat[i] == 1:
           TP += 1
    for i in range(len(y_hat)): 
        if y_actual[i] == 1 and y_actual != y_hat[i]:
           FP += 1
    for i in range(len(y_hat)): 
        if y_actual[i] == y_hat[i] == 0:
           TN += 1
    for i in range(len(y_hat)): 
        if y_actual[i] == 0 and y_actual != y_hat[i]:
           FN += 1

    return(TP, FP, TN, FN)

但它返回了我

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). How can I fix that or maybe there are the better decision?

Answer 1

我使用来自 sklearn.metrics 的 confusion_matrix，它返回我需要的矩阵。