evoFE - Evolutionary Feature Engineering

Automates feature engineering using evolutionary algorithms inspired by genetic programming. Starting from raw input features, the package evolves candidate transformation recipes through selection, crossover, and mutation, evaluating fitness via cross-validation or train/validation splits with gradient-boosted tree models ('LightGBM' or 'XGBoost'). Built-in transformers include arithmetic, logarithmic, and power operations, interaction terms, target encoding, quantile and log-based binning, principal component analysis, truncated singular value decomposition, Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction, and minimum spanning tree (MST) graph-based clustering. The evolutionary search yields an optimised feature recipe that can be applied to new data for prediction. Methods are described in McInnes et al. (2018) <doi:10.21105/joss.00861>, Ke et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-framework>, Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>, Gagolewski (2021) <doi:10.1016/j.softx.2021.100722>, Gagolewski (2026) <doi:10.32614/CRAN.package.lumbermark>, and Gagolewski (2026) <doi:10.32614/CRAN.package.deadwood>.