# How to Score an MLP Classifier

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).

• If you don't write out what you mean by "MLP", I'm going to assume you're trying to classify My Little Pony characters. It's a hard problem, now that we have 7 seasons and several movies. – Kodiologist Sep 26 '17 at 19:56