feat: feature reduction for sklearn pipeline
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MartinBernstorff commented
Why?
We would like to examine the trade-off between n_features and performance. To do that, we want to train with the 'best' [1, 2, 3, 4, 5, 6] features and so forth, and get performance for each of them.
Looks like sklearn doesn't output this information. However, mlextend does!
To get the right type of crossvalidation, we want to specify that as well. It can take a generator, like we use in the crossvalidation trainer.
- Implement sequential feature selection unit test
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