Evaluating Multiple Classifier using f2 score

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I am trying to classify some models in a binary classification. I want to classify the models with respect of score and f2 score.

For the 'score' I used the code

for name, clf in zip(models, classifiers):
    clf.fit(X_train, y_train)
    score = clf.score(X_test, y_test)
    scores.append(score)

Which gives the scores of all the models, but I am not able to do it find the f2 score of all the models. Can anyone suggest what should be the code?

machine-learning python
1 Answers

You can use the fbeta_score for this where you just set β equal to 2.

from sklearn.metrics import fbeta_score

scores = []
f2_score = []

for name, clf in zip(models, classifiers):
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    f2 = beta_score(y_test, y_pred, beta=2, average='binary')
    score = clf.score(X_test, y_test)
    scores.append(score)
    f2_score.append(f2)
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