Confusion_matrix according two indicators-CodePudding

y_true means correct target values;

Y_pred represents the probability value returned by the classifier to estimate the target

Please calculate the confusion matrix according to these two indicators.

y_true = [True,False,False,True]

y_pred = [0.15,0.97,0.24,0.88]

def func(y_true,y_pred,thresh):

I don't have a solution yet, anyone has a idea?

CodePudding user response：

You can use confusion_matrix from sklearn.metrics. All you have to do is transform y_true and y_pred to binary values.

from sklearn.metrics import confusion_matrix
def conf_m(y_true, y_pred, thresh = 0.5):
    y_true = [int(i) for i in y_true]
    y_pred = [1 if x>=thresh else 0 for x in y_pred]
    cm = confusion_matrix(y_true, y_pred)
    return cm

Without sklearn:

import numpy as np
def conf_m(y_true, y_pred, thresh = 0.5):
    y_true = [int(i) for i in y_true]
    y_pred = [1 if x>=thresh else 0 for x in y_pred]
    K = len(np.unique(y_true))
    cm = np.zeros((K, K))

    for i in range(len(y_true)):
        cm[y_true[i]][y_pred[i]]  = 1

    return cm

CodePudding user response：

def conf_m(y_true, y_pred, thresh = 0.5):
    y_true = [int(i) for i in y_true]
    y_pred = [1 if x>=thresh else 0 for x in y_pred]
    cm = confusion_matrix(y_true, y_pred)
    TP, FP, TN, FN = 0, 0, 0, 0

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

   return TP, FP, TN, FN