I am trying to understand MLP Classifiers, but would like to know what the best way to create a score is. e.g. preferably a normalized score between 0 and 1.

For instance, I looked at Scikit-learn's MLP Regressor which uses a score of

$1 – u / v$, where $u = sum(TRUE – PREDICT)^{2}$

and $v = sum (TRUE – AVGTRUE)^2 .$

see: here

I can't see a way of extending this to Classifier problems. Scikit-Learn's documentation is (to me at least) not very clear on what the Classifier score actually is, as in what the calculation is.

I would like to know a standard, or recommended, function for calculating a Classifier score (not necessarily Scikit-Learn's).

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#### Best Answer

You are essentially asking the loss function in classification setting. You may read this page in wikipedia. Here is a short version:

If the classifier is outputting a binary label in binary classification problem, 0-1 loss can be used.

If the classifier is outputting a probability number in binary classification problem, logistic loss can be used.