What are the parameters for sklearn's score function?

Question

I recently looked at a bunch of sklearn tutorials, which were all similar in that they scored the goodness of fit by:

clf.fit(X_train, y_train) clf.score(X_test, y_test) 

And it'll spit out:

0.92345... 

or some other score.

I am curious as to the parameters of the clf.score function or how it scores the model. I looked all over the internet, but can't seem to find documentation for it. Does anyone know?

Answer 1

It takes a feature matrix X_test and the expected target values y_test. Predictions for X_test are compared with y_test and either accuracy (for classifiers) or R² score (for regression estimators is returned.

This is stated very explicitly in the docstrings for score methods. The one for classification reads

Returns the mean accuracy on the given test data and labels.  Parameters ---------- X : array-like, shape = (n_samples, n_features)     Test samples.  y : array-like, shape = (n_samples,)     True labels for X.  sample_weight : array-like, shape = [n_samples], optional     Sample weights.  Returns ------- score : float     Mean accuracy of self.predict(X) wrt. y. 

and the one for regression is similar.