setup_HAL: Setup HAL Hyperparameters

In rtemis: Schema-Driven Machine Learning and Visualization

Setup HAL Hyperparameters

Description

Setup hyperparameters for Highly Adaptive Lasso training.

Usage

setup_HAL(
  max_degree = 2L,
  smoothness_orders = 1L,
  reduce_basis = NULL,
  num_knots = NULL,
  use_min = TRUE,
  nfolds = 10L,
  max_basis = 5000000L,
  seed = NULL,
  ifw = FALSE
)

Arguments

max_degree	(Tunable) Integer [1, Inf): Highest order of interaction among features that a basis function may represent.
smoothness_orders	(Tunable) Integer [0, 9]: Smoothness of the basis functions: 0 fits zero-order indicators, 1 piecewise linear splines, higher values higher-order splines.
reduce_basis	(Tunable) Optional Numeric (0, 1]: Minimum proportion of cases a basis function must be non-zero in to be kept. Applies only when smoothness_orders is 0; a search that also covers higher orders drops it from those grid cells.
num_knots	Optional Integer [1, Inf) vector: Number of knots per interaction degree, one value per degree, non-increasing. NULL generates them from max_degree and smoothness_orders.
use_min	Logical: If TRUE, select lambda.min from the internal cross-validation; if FALSE, the more heavily penalized lambda.1se.
nfolds	Integer [3, Inf): Number of folds of the internal cross-validation that selects lambda.
max_basis	Integer [1, Inf): Largest projected basis-function count that will be fit.
seed	Optional Integer: Random seed for the internal cross-validation's fold assignment. NULL leaves it drawn from the ambient RNG.
ifw	(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in classification.

Details

Both outcome types are fit with hal9001::fit_hal, which spans the outcome with a basis of indicator or spline terms over every interaction of the features up to max_degree, then fits a lasso over that basis. Regression uses the gaussian family, binary classification the binomial one; hal9001 has no multinomial family, so multiclass classification is not supported.

lambda is selected by cross-validation inside the fit and is not a search dimension. seed fixes that cross-validation's fold assignment; nfolds and use_min control it.

hal9001 takes a numeric matrix, so factors are one-hot encoded first and the encoder is re-applied at predict time.

Scaling: the basis has one function per knot per feature subset, so its size grows as C(n_features, max_degree) and, through the knots, with the number of cases – making the lasso that follows quadratic in the number of cases. Training projects the basis size up front, reports it at verbosity >= 1, warns past a million, and aborts past max_basis. max_degree is the strongest lever on that count, num_knots the next.

get_varimp() reports two measures, both aggregated over the basis functions that involve each feature:

• importance: the sum of the absolute values of their non-zero coefficients.

• max_coefficient: the largest single such absolute coefficient, separating a feature carried by one strong term from one carried by many weak ones.

plot_varimp(mod, measure = "max_coefficient") plots the second; the first is the default. Both read coefficients on the scale of the basis functions. At smoothness_orders = 0 the basis is made of indicators, so the coefficients are unit-free and directly comparable across features; at higher orders each basis function carries the units of its feature, so scale the features first if they are not already comparable.

Value

HALHyperparameters object.

Author(s)

EDG

Examples

hal_hyperparams <- setup_HAL(max_degree = 1L)
hal_hyperparams

rtemis documentation built on Oct. 20, 2026, 1:06 a.m.