[scikit-learn] Skrub 0.2.0: tabular learning made easy

[Gael Varoquaux]

Hi scikit-learn'ers

We just released skrub 0.2.0: https://skrub-data.org. This release markedly simplifies learning on complex dataframes.

# `model = tabular_learner('classifier')`

The highlight of the release is the `tabular_learner` function, which facilitates creating pipelines that readily perform machine learning on dataframes, adding preprocessing to a scikit-learn compatible learner. The function basically packs defaults and heuristics to transform all forms of dataframes to a representation that is well suited to a learner, and it can adapt these transformation: tabular_learner(HistGradientBoostingClassifier()) encodes categories differently than tabular_learner(LogisticRegression()).

The heuristics are tuned based on much benchmarking and experience shows that they give good tradeoffs. The default `tabular_learner('classifier')` is often a strong baseline.


# `transformer = TableVectorizer()`

Behind the hood, the work is done by the `skrub.TableVectorizer()`, a scikit-learn compatible transformer that facilitates combining multiple transformations on the different columns of a dataframe. The TableVectorizer is not new in the 0.2.0 release, but we have completely revamped its internals to cover really well edge cases. Indeed, one challenge is to make sure that nothing different or strange happens at test time. Actually, enforcing consistency between train-time and test-time transformation is the real value of skrub compared to using pandas or polars to do transformation.

# Increasing support of polars

We have implemented a new mechanism for supporting both pandas and polars. It has not been applied on all the codebase, hence the support is still imperfect. However, we are seeing increasing support for polars in skrub, and our goal in the short term is to provide rock-solid polar support.

Try skrub out! It's still young, but in mind opinion, it provides a lot of value to tabular learning.

Cheers,

Gaël