為什么 cross_val_score 與我手動計算時不同？

Question

這是可重現的示例代碼：

from numpy import mean
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_validate
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import balanced_accuracy_score

# define dataset
X, y = make_classification(n_samples=1000, weights = [0.3,0.7], n_features=100, n_informative=75, random_state=0)
# define the model
model = RandomForestClassifier(n_estimators=10, random_state=0)
# evaluate the model
n_splits=10
cv = StratifiedShuffleSplit(n_splits, random_state=0)
n_scores = cross_validate(model, X, y, scoring='balanced_accuracy', cv=cv, n_jobs=-1, error_score='raise')
# report performance
print('Accuracy: %0.4f' % (mean(n_scores['test_score'])))

bal_acc_sum = []
for train_index, test_index in cv.split(X,y):
    model.fit(X[train_index], y[train_index])                                      
    bal_acc_sum.append(balanced_accuracy_score(model.predict(X[test_index]),y[test_index]))

print(f"Accuracy: %0.4f" % (mean(bal_acc_sum)))

結果：

Accuracy: 0.6737
Accuracy: 0.7113

我自己計算的准確性的結果總是高於交叉驗證給我的結果。 但它應該是一樣的還是我錯過了什么？ 相同的度量，相同的拆分（KFold 帶來相同的結果），相同的固定 model（其他型號表現相同），相同的隨機 state，但結果不同？

Answer 1

這是因為，在您的手動計算中，您已經翻轉了 balance_accuracy_score 中balanced_accuracy_score的順序，這很重要 - 它應該是(y_true, y_pred) ( docs )。

更改此設置，您的手動計算將變為：

bal_acc_sum = []
for train_index, test_index in cv.split(X,y):
    model.fit(X[train_index], y[train_index])                                      
    bal_acc_sum.append(balanced_accuracy_score(y[test_index], model.predict(X[test_index])))  # change order of arguments here

print(f"Accuracy: %0.4f" % (mean(bal_acc_sum)))

結果：

Accuracy: 0.6737

和

import numpy as np
np.all(bal_acc_sum==n_scores['test_score'])
# True