Vision: where is skrub heading?#
The goal of skrub is to facilitate building and deploying machine-learning models on tables: pandas dataframe, SQL databases…
Skrub is high-level, with a philosophy and an API matching that of scikit-learn. It strives to bridge the world of databases to that of machine-learning, enabling imperfect assembly and representations of the data when it is noisy, using the downstream target to predict to guide assembly when possible (supervised learning for data assembly).
In the long term, as skrub is built on higher-level APIs, it will make it easier for data-scientists to use efficient database patterns and backends.
Skrub seeks tradeoffs in terms of flexibility: its high-level APIs are by construction restrictive compared to directly manipulating dataframes. This is by design, as skrub does not aim to replace tools such as Pandas, Ibis, DuckDB.
To make things simpler, skrub uses defaults that are chosen empirically to
give good machine learning, even though these are sometimes heuristic, as
In an open-source project, roadmaps can be whishful thinking: things happen in an iterative way, often guided by the community.
We however decided to communicate on what we would like to do in the next 6 months to give a better idea of the vision.
From shorter term to longer term:
TableVectorizerfast, robust, and easy to tune (in the sense of hyper-parameter tuning)
Add a Join-aggregator object, to do feature augmentation on one-to-many correspondences
Support time series (eg in the aggregations)
Interpolator join to join across multiple columns without exact correspondences in the keys
Release (yes we are not planning to release very soon)
Data namespaces, lazy data loading, out of core computing using database engines (eg duckdb)
Join discovery to work in data lakes where the tables are not in a clean relational database
Automatic feature synthesis in databases, building on the assembling features