tick.preprocessing¶This module provides several preprocessing utilities, in the form of transformer classes that change raw feature vectors into a suitable representation for some learners. These transformers should be scikit-learn compatible, whenever possible.
The FeaturesBinarizer binarizes all
continuous features found in features matrix. This transformer is particularly
useful whenever using the ProxBinarsity
penalization for supervised linear learning see tick.linear_model.
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Transforms continuous data into bucketed binary data. |
This module also provides preprocessor specific to longitudinal features with a similar API to scikit-learn preprocessors.
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Transforms longitudinal exposure features to add the corresponding product features. |
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Transforms longitudinal exposure features to add columns representing lagged features. |
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Longitudinal data preprocessor which filters out samples for which all |