yano_pres/prep/10pipeline/q

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Again there are a couple modifiers possible
<l2st>
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)
</l2st>
modifiers interact with each other
For example: normalize('minmax'), split
->train set always below 1, but no guarantees for the test set