Again there are a couple modifiers possible nonconst->remove constant features shuffle normalize('zscore'/'minmax') cut(10)->at most 10 datasets split->train test split, all anomalies in test set crossval(5)->similar to split, but do multiple times (crossvalidation) modifiers interact with each other For example: normalize('minmax'), split ->train set always below 1, but no guarantees for the test set