Is there any way to calculate residual deviance of a scikit-learn logistic regression model? This is a standard output from R model summaries, but I couldn't find it any of sklearn's documentation.

1. As suggested by @russell-richie, it should be model.predict_proba
2. Don't forget the argument normalize=False in function metrics.log_loss() to return the sum of the per-sample losses.

So to complete @ingo's answer, to obtain the model deviance with sklearn.linear_model.LogisticRegression, you can compute:

def deviance(X, y, model):return 2*metrics.log_loss(y, model.predict_proba(X), normalize=False)