Comparison table

Use this page to filter representations by capability. It is a static reference; for a live, queryable view use list_representations(...) against the registry. The registry is the single source of truth, and these tables mirror it.

Reading the columns

Key: The string you pass to numerical_method, categorical_method, or per-feature config.
Scope: univariate (one column) or multivariate (several columns jointly).
Target: forbidden, optional (used when target_aware=True), or required.
Adaptive: Supports data-driven width selection between min_output_dim and max_output_dim.
Penalty: Exposes get_penalty_matrix() for smoothing penalties.
Selectable: Can be chosen through Preprocessor as a per-column method.

Numerical: scalers and simple transforms

Method	Key	Scope	Target	Selectable
Standardization	`standardization`	univariate	forbidden	yes
Min-max scaling	`minmax`	univariate	forbidden	yes
Robust scaling	`robust`	univariate	forbidden	yes
Quantile transform	`quantile`	univariate	forbidden	yes
Polynomial features	`polynomial`	univariate	forbidden	yes
Box-Cox	`box-cox`	univariate	forbidden	yes
Yeo-Johnson	`yeo-johnson`	univariate	forbidden	yes
Passthrough	`none`	univariate	forbidden	yes

Numerical: splines

Method	Key	Scope	Target	Adaptive	Penalty	Selectable
B-spline	`bspline`	univariate	optional	yes	no	yes
M-spline	`mspline`	univariate	optional	yes	no	yes
I-spline	`ispline`	univariate	optional	yes	no	yes
Cubic regression spline	`cubicspline`	univariate	optional	yes	yes	yes
Natural cubic spline	`naturalspline`	univariate	optional	yes	yes	yes
Penalized spline (P-spline)	`pspline`	univariate	forbidden	yes	yes	yes
Tensor-product spline	`tensorspline`	multivariate	forbidden	yes	yes	no
Thin-plate spline	`tprs`	multivariate	forbidden	no	yes	no

Note

The multivariate splines (tensorspline, tprs) model several inputs jointly and are used standalone, not selected per column through Preprocessor. The alias thinplate resolves to tprs.

Numerical: feature maps

Method	Key	Scope	Target	Adaptive	Selectable
RBF expansion	`rbf`	univariate	optional	yes	yes
ReLU expansion	`relu`	univariate	optional	yes	yes
Sigmoid expansion	`sigmoid`	univariate	optional	yes	yes
Tanh expansion	`tanh`	univariate	optional	yes	yes
Fourier features	`fourier`	univariate	forbidden	no	yes
Random Fourier features	`rff`	multivariate	forbidden	no	no
Nyström kernel map	`nystroem`	multivariate	forbidden	no	no

Numerical: discretization

Method	Key	Scope	Target	Adaptive	Selectable
Numeric binning	`custombin`	univariate	forbidden	no	yes
Piecewise-linear encoding (PLE)	`ple`	univariate	required	yes	yes

Important

PLE is the only numerical method that requires the target. It always places its bins against y, so it must be fit with a target and is best used with cross-fitting. See Target awareness.

Categorical

Method	Key	Scope	Target	Selectable
Ordinal (integer) encoding	`int`	univariate	forbidden	yes
One-hot encoding	`one-hot`	univariate	forbidden	yes
One-hot from ordinal	`onehot_from_ordinal`	univariate	forbidden	yes
Pretrained language embedding	`pretrained`	univariate	forbidden	yes
Passthrough	`none`	univariate	forbidden	yes

Note

pretrained requires the optional embeddings extra. The alias ohe resolves to one-hot.

Where to go next

Choosing a method for guidance on which of these to reach for.
Splines, Feature maps, Binning and PLE, Categorical for the details.