Hyperparameter and configuration guide

Every numerical transformer in PreTab shares a small set of hyperparameters. This guide explains what each one does, the default it ships with, and how to choose a value that suits your data. If there is one setting worth learning first, it is output_dim, which controls how wide each feature becomes after transformation.

Note

When you work through the Preprocessor, its single output_dim (default 7) is forwarded to every numerical method. The per-transformer defaults listed below therefore only take effect when you build a transformer directly, for example RBFExpansionTransformer().

The output_dim width knob

output_dim is the main capacity control. It sets the number of non-bias output columns produced for each input feature: bins for PLE and binning, centers for the feature maps, and basis functions for the splines. A larger value captures finer structure in a feature, at the cost of more columns and a higher chance of overfitting. A smaller value is more compact and regularises the representation.

The defaults are aligned on a moderate value of 6. It is expressive enough for most features while staying compact, and it clears the minimum width that every spline basis requires. The tensor-product spline is the single exception: its columns multiply across marginal dimensions, so it defaults to its smallest valid width to keep the output from exploding.

Method	Class	Default	Minimum (floor)
PLE	PLETransformer	6	1 (upper bound on bins; actual count is data-dependent)
RBF map	RBFExpansionTransformer	6	1
ReLU map	ReLUExpansionTransformer	6	1
Sigmoid map	SigmoidExpansionTransformer	6	1
Tanh map	TanhExpansionTransformer	6	1
Cubic spline	CubicSplineTransformer	6	3 (3 polynomial terms + interior knots)
Natural cubic	NaturalCubicSplineTransformer	6	2 (places output_dim + 1 knots)
B/M/I splines	BSplineTransformer, MSplineTransformer, ISplineTransformer	6	degree + 1 (=4), capped at 50
P-spline	PSplineTransformer	6	degree + 1 (=4)
Tensor product	TensorProductSplineTransformer	4	degree + 1 (=4) per marginal
Thin-plate	ThinPlateSplineTransformer	6	1
Preprocessor (shared)	Preprocessor	7	overrides the per-transformer defaults above

Warning

Each spline enforces its own minimum width. Requesting fewer basis functions than the floor in the table raises an error at fit time instead of silently clamping, so keep output_dim at or above that floor. The floor is degree + 1 for the B, M, I, P-spline, and tensor-product bases.

Tip

For the tensor-product spline the width grows as the product across marginals. A 2-D input with output_dim=4 already produces 4 × 4 = 16 columns, so raise it in small steps and keep an eye on the total column count.

Adaptive sizing

PLE and the feature maps can size each feature from the data instead of using one fixed width. This helps when your features differ a lot in complexity and you would rather not tune output_dim by hand.

adaptive

When True, the width for each feature is chosen from the data and kept inside [min_output_dim, max_output_dim]. Fixed-width methods such as the plain scalers ignore this flag.

min_output_dim, max_output_dim

The lower and upper bounds that apply only when adaptive=True. They are ignored otherwise.

Target-aware placement

Some methods can place their bins, centers, or knots using the target y. This tends to sharpen the representation where the target actually changes, at the cost of needing labels at fit time.

target_aware

Whether placement uses the target. PLE is inherently target-aware. Every other family defaults to target_aware=False, which is fully unsupervised and fits without y.

placement_strategy

How units are placed. The valid values depend on target_aware, as shown below.

target_aware	Allowed placement_strategy	Default when unset
True	cart, lightgbm	cart
False	uniform, quantile	quantile

Warning

The two rows of this table are mutually exclusive. Combining them, for example target_aware=True with placement_strategy="quantile", raises an error. Leave placement_strategy unset to get the sensible default for whichever mode you picked.

task

Either "regression" or "classification". It is only consulted by target-aware placement, which uses it to fit the selector against y.

Spline-specific parameters

degree

Degree of the spline basis, where 3 is cubic. It also sets the output_dim floor of degree + 1 for the B, M, I, P-spline, and tensor-product bases, so a lower degree lowers the minimum width.

include_bias

When True, a constant intercept column is prepended to the output. The bias term is left unpenalised.

Reproducibility

random_state

Seeds the target-aware selectors and any stochastic placement so that repeated fits produce identical output. Set it to an integer whenever you need deterministic results, for example in tests or published experiments.