Version 1.8.X#

Version 1.8.0#

Deployed: 26th May 2024

Contributors#

In this release, we make some breaking changes. The DecisionTreeEncoder() does not have the encoding pipeline any more. In its place, we now added an encoding_dict_ parameter that stores the mappings from category to predictions of the decision tree. This allowed us to implement in addition a way to handle unseen categories and the method inverse_transform.

We also expanded the functionality of the DecisionTreeDiscretiser(), which can now replace the continuous attributes with the decision tree predictions, interval limits, or bin number.

In addition, we introduce a new transformer, the DecisionTreeFreatures(), which adds new features to the data, resulting from predictions of decision trees trained on one or more features.

The classes from the module outliers can now automatically select the limit for the boundaries for outliers.

Finally, we have updated and expanded various pages of our documentation.

Thank you very much to all contributors to this release and to Vasco Schiavo and Gleb Levitski for actively reviewing many of our PRs.

If you value what we do, please consider sponsoring us, so that we can keep updating Feature-engine at a fast pace.

New#

  • DecisionTreeFeatures is a new transformer from the creation module that adds features based of predictions of decision trees (Soledad Galli)

Enhancements#

  • DecisionTreeEncoder now supports encodings for unseen categories, inverse_transform, and provides an encoding dictionary instead of the pipeline (Soledad Galli, Gleb Levitski and Lorenzo Vitali )

  • The DecisionTreeDiscretiser() can now replace the continuous attributes with the decision tree predictions, interval limits, or bin number (Soledad Galli)

  • The OutlierTrimmer() and Winsorizer() can now adjust the strength of the outlier search automatically based of the statistical method (param fold="auto") (Gleb Levitski)

Documentation#