feature-engineering

py-evoFE is an open-source Python library that uses genetic algorithms to automate and optimize feature engineering for tabular machine learning datasets, with scikit-learn compatibility and Polars for performance.

The paper introduces Crafter, an agent for corrective feature discovery that mines the residual of frozen black-box forecasters using compositional search and LLM-generated features, achieving up to 27% error reduction across six datasets and backbones.

A Kaggle Grandmaster is offering a free course covering the entire machine learning process, including EDA, decision trees, gradient boosting, feature engineering, time series, and competition strategies.

This arXiv preprint challenges the 'Garbage In, Garbage Out' heuristic, arguing that aggressive manual data cleaning can limit predictive performance in high-dimensional tabular data by reducing dimensionality needed to triangulate latent drivers.