CODEWITHSUNDEEP
pythonpython-3.xscikit-learncross-validation
Akhmad Zaki
Akhmad Zaki

Reputation: 433

Cross Validation for Different Metrics

When I am doing cross validation using Python's Sklearn and take the score of different metrics (accuracy, precision, etc.) like this:

result_accuracy = cross_val_score(classifier, X_train, y_train, scoring='accuracy', cv=10)
result_precision = cross_val_score(classifier, X_train, y_train, scoring='precision', cv=10)
result_recall = cross_val_score(classifier, X_train, y_train, scoring='recall', cv=10)
result_f1 = cross_val_score(classifier, X_train, y_train, scoring='f1', cv=10)

Did every execution of cross_val_score() function for different metrics made the same 10 folds of the training data or not? If not, do I need to make the general 10-folds first using KFold, like this:

seed = 7
kf = KFold(n_splits=10, random_state=seed)

result_accuracy = cross_val_score(classifier, X_train, y_train, scoring='accuracy', cv=kf)
result_precision = cross_val_score(classifier, X_train, y_train, scoring='precision', cv=kf)
result_recall = cross_val_score(classifier, X_train, y_train, scoring='recall', cv=kf)
result_f1 = cross_val_score(classifier, X_train, y_train, scoring='f1', cv=kf)

Upvotes: 0

Views: 703

Answers (1)

Vivek Kumar
Vivek Kumar

Reputation: 36599

Use random_state param in cross_val_score to set the same splitting each time.

result_accuracy = cross_val_score(classifier, X_train, y_train, scoring='accuracy', cv=10, random_state=42)

Upvotes: 1

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