generic_fit(X,
    features = Symbol[],
    args...;
    ignore::Bool = true,
    ordered_factor::Bool = false,
    feature_mapper,
    kwargs...,
)

Given a feature_mapper (see definition below), this method applies feature_mapper across a specified subset of categorical columns in X and returns a dictionary whose keys are the feature names, and each value is the corresponding level‑to‑value mapping produced by feature_mapper.

In essence, it spares effort of looping over each column and applying the feature_mapper function manually as well as handling the feature selection logic.

Arguments

• X: A table where the elements of the categorical features have scitypes Multiclass or OrderedFactor

• features=[]: A list of names of categorical features given as symbols to exclude or in clude from encoding, according to the value of ignore, or a single symbol (which is treated as a vector with one symbol), or a callable that returns true for features to be included/excluded.

• ignore=true: Whether to exclude or include the features given in features

• ordered_factor=false: Whether to encode OrderedFactor or ignore them

• feature_mapper: function that, for a given vector (eg, corresponding to a categorical column from the dataset X), produces a mapping from each category level name in this vector to a scalar or vector according to specified transformation logic.

Note

• Any additional arguments (whether keyword or not) provided to this function are passed to the feature_mapper function which is helpful when feature_mapper requires additional arguments to compute the mapping (eg, hyperparameters).

Returns

• mapping_per_feat_level: Maps each level for each feature in a subset of the categorical features of X into a scalar or a vector.

• encoded_features: The subset of the categorical features of X that were encoded

generic_transform(
    X,
    mapping_per_feat_level;
    single_feat::Bool = true,
    ignore_unknown::Bool = false,
    use_levelnames::Bool = false,
    custom_levels = nothing,
    ensure_categorical::Bool = false,
)

Apply a per‐level feature mapping to selected categorical columns in X, returning a new table of the same type.

Arguments

• X: A table where the elements of the categorical features have scitypes Multiclass or OrderedFactor

• mapping_per_feat_level::Dict{Symbol,Dict}: A dict whose keys are feature names (Symbol) and values are themselves dictionaries mapping each observed level to either a scalar (if single_feat=true) or a fixed‐length vector (if single_feat=false). Only columns whose names appear in mapping_per_feat_level are transformed; others pass through unchanged.

• single_feat::Bool=true: If true, each input level is mapped to a single scalar feature; if false, each input level is mapped to a length‑k vector, producing k output columns.

• ignore_unknown::Bool=false: If false, novel levels in X (not seen during fit) will raise an error; if true, novel levels will be left unchanged (identity mapping).

• use_levelnames::Bool=false: When single_feat=false, controls naming of the expanded columns: true: use actual level names (e.g. :color_red, :color_blue), false: use numeric indices (e.g. :color_1, :color_2).

• custom_levels::Union{Nothing,Vector}: If not nothing, overrides the names of levels used to generate feature names when single_feat=false.

• ensure_categorical::Bool=false: Only when single_feat=true and if true, preserves the categorical type of the column after recoding (eg, feature should still be recognized as Multiclass after transformation)

Returns

A new table of potentially similar to X but with categorical columns transformed according to mapping_per_feat_level.