For many problems, feature normalisation and selection is a
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 (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).