{
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  "Package": "evoFE",
  "Type": "Package",
  "Title": "Evolutionary Feature Engineering",
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  "Authors@R": "person(\"Gustavo\", \"Pereira\", role = c(\"aut\", \"cre\"), email = \"tanopereira@gmail.com\")",
  "Description": "Automates feature engineering using evolutionary\nalgorithms inspired by genetic programming. Starting from raw\ninput features, the package evolves candidate transformation\nrecipes through selection, crossover, and mutation, evaluating\nfitness via cross-validation or train/validation splits with\ngradient-boosted tree models ('LightGBM' or 'XGBoost').\nBuilt-in transformers include arithmetic, logarithmic, and\npower operations, interaction terms, target encoding, quantile\nand log-based binning, principal component analysis, truncated\nsingular value decomposition, Uniform Manifold Approximation\nand Projection (UMAP) dimensionality reduction, and minimum\nspanning tree (MST) graph-based clustering. The evolutionary\nsearch yields an optimised feature recipe that can be applied\nto new data for prediction. Methods are described in McInnes et\nal. (2018) <doi:10.21105/joss.00861>, Ke et al. (2017)\n<https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-framework>,\nChen and Guestrin (2016) <doi:10.1145/2939672.2939785>,\nGagolewski (2021) <doi:10.1016/j.softx.2021.100722>, Gagolewski\n(2026) <doi:10.32614/CRAN.package.lumbermark>, and Gagolewski\n(2026) <doi:10.32614/CRAN.package.deadwood>.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "VignetteBuilder": "knitr",
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  "Packaged": {
    "Date": "2026-06-09 19:21:03 UTC",
    "User": "root"
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  "Author": "Gustavo Pereira [aut, cre]",
  "Maintainer": "Gustavo Pereira <tanopereira@gmail.com>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-06-09 15:50:14 UTC",
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  "_published": "2026-06-09T19:24:23.506Z",
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    "apply_gene",
    "apply_individual",
    "create_gene",
    "create_individual",
    "create_transformer",
    "crossover",
    "evaluate_fitness",
    "evo_transformers",
    "evolve_features",
    "gene_to_formula",
    "gene_to_state_formula",
    "individual_to_recipe_string",
    "initialize_population",
    "mutate",
    "predict_model",
    "union_crossover"
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  "_help": [
    {
      "page": "apply_gene",
      "title": "Apply a single gene to a dataset",
      "topics": [
        "apply_gene"
      ]
    },
    {
      "page": "apply_individual",
      "title": "Apply an entire individual's recipe to data",
      "topics": [
        "apply_individual"
      ]
    },
    {
      "page": "create_gene",
      "title": "Create a single gene",
      "topics": [
        "create_gene"
      ]
    },
    {
      "page": "create_individual",
      "title": "Create an individual",
      "topics": [
        "create_individual"
      ]
    },
    {
      "page": "create_transformer",
      "title": "Create a transformer definition",
      "topics": [
        "create_transformer"
      ]
    },
    {
      "page": "crossover",
      "title": "Crossover two individuals",
      "topics": [
        "crossover"
      ]
    },
    {
      "page": "evaluate_fitness",
      "title": "Evaluate the fitness of an individual",
      "topics": [
        "evaluate_fitness"
      ]
    },
    {
      "page": "evo_transformers",
      "title": "Built-in feature transformers",
      "topics": [
        "evo_transformers"
      ]
    },
    {
      "page": "evolve_features",
      "title": "Run evolutionary feature engineering",
      "topics": [
        "evolve_features"
      ]
    },
    {
      "page": "gene_to_formula",
      "title": "Convert a gene to a formula string",
      "topics": [
        "gene_to_formula"
      ]
    },
    {
      "page": "gene_to_state_formula",
      "title": "Convert a gene to a formula string for state caching (ignoring component index)",
      "topics": [
        "gene_to_state_formula"
      ]
    },
    {
      "page": "individual_to_recipe_string",
      "title": "Convert an individual to a recipe string of formulas",
      "topics": [
        "individual_to_recipe_string"
      ]
    },
    {
      "page": "initialize_population",
      "title": "Initialize a population",
      "topics": [
        "initialize_population"
      ]
    },
    {
      "page": "mutate",
      "title": "Mutate an individual",
      "topics": [
        "mutate"
      ]
    },
    {
      "page": "predict_model",
      "title": "Predict target values using the fully evolved model",
      "topics": [
        "predict_model"
      ]
    },
    {
      "page": "predict.evo_recipe",
      "title": "Apply feature engineering recipe to new data",
      "topics": [
        "predict.evo_recipe"
      ]
    },
    {
      "page": "union_crossover",
      "title": "Union Crossover of two individuals",
      "topics": [
        "union_crossover"
      ]
    }
  ],
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    "jsonlite",
    "lattice",
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      "source": "evoFE.Rmd",
      "filename": "evoFE.html",
      "title": "Getting Started with evoFE",
      "engine": "knitr::rmarkdown",
      "headings": [
        "What is evoFE?",
        "How it works",
        "Installation",
        "Quick Start — Binary Classification",
        "Applying the recipe to new data",
        "Regression",
        "Multiclass Classification",
        "Transformer Reference",
        "Arithmetic (numeric → numeric)",
        "Group-by aggregations (mixed → numeric)",
        "Encoding & binning",
        "Dimensionality reduction (numeric → numeric)",
        "Manifold & Graph Learning (numeric → categorical/numeric)",
        "Hierarchical Features (Gene Chaining)",
        "Understanding the Output",
        "Inspecting the recipe",
        "Evaluation Strategies",
        "Tuning Parameters",
        "Key parameters for evolve_features()",
        "Practical advice",
        "Reproducibility",
        "End-to-End Example: Train/Test Split",
        "Session Info"
      ],
      "created": "2026-06-09 15:50:14",
      "modified": "2026-06-09 15:50:14",
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