如何显示混淆矩阵和每个交叉验证折叠的报告（召回率，精度，fmeasure）

Question

I am trying to perform 10 fold cross validation in python. I know how to calculate the confusion matrix and the report for split test(example split 80% training and 20% testing). But the problem is I don't know how to calculate the confusion matrix and report for each folds for example when fold-10, I just know code for average accuracy.

Answer 1

Here is a reproducible example with the breast cancer data and 3-fold CV for simplicity:

from sklearn.datasets import load_breast_cancer
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.model_selection import KFold

X, y = load_breast_cancer(return_X_y=True)
n_splits = 3
kf = KFold(n_splits=n_splits, shuffle=True)
model = DecisionTreeClassifier()

for train_index, val_index in kf.split(X):
    model.fit(X[train_index], y[train_index])
    pred = model.predict(X[val_index])
    print(confusion_matrix(y[val_index], pred))
    print(classification_report(y[val_index], pred))

The result is 3 confusion matrices & classification reports, one per CV fold:

[[ 63   9]
 [ 10 108]]
              precision    recall  f1-score   support

           0       0.86      0.88      0.87        72
           1       0.92      0.92      0.92       118

   micro avg       0.90      0.90      0.90       190
   macro avg       0.89      0.90      0.89       190
weighted avg       0.90      0.90      0.90       190

[[ 66   8]
 [  6 110]]
              precision    recall  f1-score   support

           0       0.92      0.89      0.90        74
           1       0.93      0.95      0.94       116

   micro avg       0.93      0.93      0.93       190
   macro avg       0.92      0.92      0.92       190
weighted avg       0.93      0.93      0.93       190

[[ 59   7]
 [  8 115]]
              precision    recall  f1-score   support

           0       0.88      0.89      0.89        66
           1       0.94      0.93      0.94       123

   micro avg       0.92      0.92      0.92       189
   macro avg       0.91      0.91      0.91       189
weighted avg       0.92      0.92      0.92       189