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>.
| Version: |
0.1.0 |
| Imports: |
data.table, lightgbm, xgboost, stats, digest, uwot, quitefastmst, genieclust |
| Suggests: |
RhpcBLASctl, testthat, knitr, rmarkdown, lumbermark, deadwood |
| Published: |
2026-06-09 |
| DOI: |
10.32614/CRAN.package.evoFE (may not be active yet) |
| Author: |
Gustavo Pereira [aut, cre] |
| Maintainer: |
Gustavo Pereira <tanopereira at gmail.com> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
evoFE results |
Documentation:
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