Feature Normalisation and Selection

For many problems, feature normalisation and selection is a

Simple Normalisations

Fill in NaNs and Infs: the checkfinite() learner does this. This learner does not use any of its input features: it always returns the same model.


Checkout the functions zscore() if you have a feature matrix or the zscore_normalise() learner.

Stepwise Discriminant Analysis

Stepwise Discriminant Analysis (SDA) is a simple feature selection method. It is supervised and independent of the downstream classifier.

Important Note: SDA does not work well if your features are linearly dependent. Filter out linearly dependent features before calling SDA (use linearly_dependent_features).