setup_LightRuleFit: Setup LightRuleFit Hyperparameters

In rtemis: Schema-Driven Machine Learning and Visualization

Setup LightRuleFit Hyperparameters

Description

Setup hyperparameters for LightRuleFit training.

Usage

setup_LightRuleFit(
  nrounds = 200L,
  num_leaves = 32L,
  max_depth = 4L,
  learning_rate = 0.1,
  bagging_fraction = 0.666,
  bagging_freq = 1L,
  lambda_l1 = 0,
  lambda_l2 = 0,
  objective = NULL,
  force_col_wise = TRUE,
  tree_learner = "serial",
  top_k = NULL,
  feature_fraction = 1,
  feature_fraction_bynode = 1,
  feature_fraction_seed = 2L,
  linear_tree = FALSE,
  linear_lambda = NULL,
  max_cat_threshold = 32L,
  min_data_per_group = 32L,
  alpha = NULL,
  deterministic = FALSE,
  objective_seed = 5L,
  force_row_wise = FALSE,
  histogram_pool_size = -1,
  pos_bagging_fraction = 1,
  neg_bagging_fraction = 1,
  bagging_seed = 3L,
  cegb_tradeoff = 1,
  cegb_penalty_split = 0,
  cegb_penalty_feature_lazy = NULL,
  cegb_penalty_feature_coupled = NULL,
  use_quantized_grad = FALSE,
  num_grad_quant_bins = NULL,
  quant_train_renew_leaf = NULL,
  stochastic_rounding = NULL,
  monotone_constraints = NULL,
  monotone_constraints_method = NULL,
  monotone_penalty = NULL,
  interaction_constraints = NULL,
  feature_contri = NULL,
  min_data_in_leaf = 20L,
  min_sum_hessian_in_leaf = 0.001,
  min_gain_to_split = 0,
  max_delta_step = 0,
  path_smooth = 0,
  extra_trees = FALSE,
  extra_seed = 6L,
  max_bin = 255L,
  min_data_in_bin = 3L,
  use_missing = TRUE,
  zero_as_missing = FALSE,
  cat_l2 = 10,
  cat_smooth = 10,
  max_cat_to_onehot = 4L,
  boosting = "gbdt",
  data_sample_strategy = "bagging",
  drop_rate = NULL,
  max_drop = NULL,
  skip_drop = NULL,
  uniform_drop = NULL,
  xgboost_dart_mode = NULL,
  drop_seed = NULL,
  top_rate = NULL,
  other_rate = NULL,
  tweedie_variance_power = NULL,
  fair_c = NULL,
  poisson_max_delta_step = NULL,
  sigmoid = NULL,
  boost_from_average = NULL,
  reg_sqrt = NULL,
  ifw_lightgbm = FALSE,
  alpha_glmnet = 1,
  lambda_glmnet = NULL,
  ifw_glmnet = FALSE,
  ifw = FALSE
)

Arguments

nrounds	(Tunable) Integer [1, Inf): Number of boosting rounds.
num_leaves	(Tunable) Integer [1, Inf): Maximum number of leaves in one tree.
max_depth	(Tunable) Integer: Maximum depth of trees.
learning_rate	(Tunable) Numeric (0, 1]: Learning rate.
bagging_fraction	(Tunable) Numeric (0, 1]: Fraction of cases sampled per tree.
bagging_freq	(Tunable) Integer [1, Inf): Resample every this many iterations.
lambda_l1	(Tunable) Numeric [0, Inf): L1 regularization.
lambda_l2	(Tunable) Numeric [0, Inf): L2 regularization.
objective	Optional Character: Objective function. NULL = set from outcome type.
force_col_wise	Logical: Force column-wise histogram building (CPU only).
tree_learner	Character {"serial", "feature", "data", "voting"}: Tree learner type.
top_k	(Tunable) Optional Integer [1, Inf): Features each machine votes for in the voting tree learner. Requires tree_learner = "voting". NULL uses LightGBM's default of 20.
feature_fraction	(Tunable) Numeric (0, 1]: Fraction of features sampled per tree.
feature_fraction_bynode	(Tunable) Numeric (0, 1]: Fraction of features sampled at each node, on top of the per-tree fraction.
feature_fraction_seed	Integer: Random seed for feature sampling.
linear_tree	(Tunable) Logical: Fit linear models at leaves.
linear_lambda	(Tunable) Optional Numeric [0, Inf): L2 penalty on the linear models fitted at the leaves. Requires linear_tree = TRUE. NULL uses LightGBM's default of 0.
max_cat_threshold	(Tunable) Integer [1, Inf): Maximum number of split points for categorical features.
min_data_per_group	(Tunable) Integer [1, Inf): Minimum number of cases per categorical group.
alpha	(Tunable) Optional Numeric (0, Inf): Huber delta or quantile level. Requires objective to be "huber" or "quantile". NULL uses LightGBM's default of 0.9.
deterministic	Logical: Force reproducible results at some cost in speed.
objective_seed	Integer: Random seed for the objective, where it draws.
force_row_wise	Logical: Force row-wise histogram building. Cheaper in memory than column-wise, slower per iteration.
histogram_pool_size	Numeric: Memory cap in MB for the histogram cache; -1 leaves it uncapped.
pos_bagging_fraction	(Tunable) Numeric (0, 1]: Fraction of positive cases sampled per tree, for a binary outcome.
neg_bagging_fraction	(Tunable) Numeric (0, 1]: Fraction of negative cases sampled per tree, for a binary outcome.
bagging_seed	Integer: Random seed for bagging.
cegb_tradeoff	(Tunable) Numeric [0, Inf): Overall multiplier on every cost-efficiency penalty. Its scale is not comparable between boosting modes.
cegb_penalty_split	(Tunable) Numeric [0, Inf): Cost charged per split, in units of loss reduction.
cegb_penalty_feature_lazy	Optional List: Per-feature cost charged the first time a case uses it, in [0, Inf). One vector applied to every tree, or one vector per tree. NULL charges no cost.
cegb_penalty_feature_coupled	Optional List: Per-feature cost charged the first time any case uses it, in [0, Inf). One vector applied to every tree, or one vector per tree. NULL charges no cost.
use_quantized_grad	Logical: Discretize gradients before finding splits. Faster, and changes the fit.
num_grad_quant_bins	(Tunable) Optional Integer [2, Inf): Bins the gradients are discretized into. Requires use_quantized_grad = TRUE. NULL uses LightGBM's default of 4.
quant_train_renew_leaf	Optional Logical: Recompute leaf values from the exact gradients after each iteration. Requires use_quantized_grad = TRUE. NULL uses LightGBM's default of FALSE.
stochastic_rounding	Optional Logical: Round gradients stochastically rather than to nearest. Requires use_quantized_grad = TRUE. NULL uses LightGBM's default of TRUE.
monotone_constraints	Optional Integer [-1, 1] vector: Per-feature monotonicity: 1 increasing, -1 decreasing, 0 unconstrained. One value per feature. NULL constrains no feature.
monotone_constraints_method	Optional Character {"basic", "intermediate", "advanced"}: How monotonicity is enforced. Requires monotone_constraints. NULL uses LightGBM's default, "basic".
monotone_penalty	(Tunable) Optional Numeric [0, Inf): Depth penalty on splits that would break monotonicity. Requires monotone_constraints. NULL uses LightGBM's default of 0.
interaction_constraints	Optional Character: Feature groups permitted to interact, in LightGBM's own bracketed form, e.g. "[0,1],[2,3]". NULL permits every interaction.
feature_contri	Optional Numeric vector: Per-feature multiplier on split gain, one value per feature. NULL uses 1 for every feature.
min_data_in_leaf	(Tunable) Integer [0, Inf): Minimum number of cases in a leaf.
min_sum_hessian_in_leaf	(Tunable) Numeric [0, Inf): Minimum sum of hessians in a leaf: the smooth counterpart of a case count.
min_gain_to_split	(Tunable) Numeric [0, Inf): Minimum loss reduction a split must buy to be made.
max_delta_step	(Tunable) Numeric: Cap on each leaf's output; 0 or less leaves it uncapped.
path_smooth	(Tunable) Numeric [0, Inf): Smooth each leaf towards its parent, more strongly the fewer cases it holds.
extra_trees	(Tunable) Logical: Choose one split threshold per feature at random rather than the best.
extra_seed	Integer: Random seed for the thresholds extra_trees draws.
max_bin	(Tunable) Integer [2, Inf): Most bins a feature is discretized into. Fewer is faster and more regularized.
min_data_in_bin	(Tunable) Integer [1, Inf): Minimum number of cases per bin.
use_missing	Logical: Handle missing values. FALSE ignores them.
zero_as_missing	Logical: Treat zeros as missing, rather than only NA.
cat_l2	(Tunable) Numeric [0, Inf): L2 regularization on a categorical split.
cat_smooth	(Tunable) Numeric [0, Inf): Smoothing over categorical levels, which reduces the effect of rare ones.
max_cat_to_onehot	(Tunable) Integer [1, Inf): Below this many levels, split one-versus-rest rather than by partition.
boosting	Character {"gbdt", "rf", "dart"}: Boosting algorithm: gradient boosting, random forest, or DART (dropout).
data_sample_strategy	Character {"bagging", "goss"}: How cases are subsampled: at random, or by gradient magnitude (GOSS). GOSS cannot be combined with bagging, so it requires bagging_fraction = 1.
drop_rate	(Tunable) Optional Numeric [0, 1]: Fraction of existing trees to drop per iteration. Requires boosting = "dart". NULL uses LightGBM's default of 0.1.
max_drop	(Tunable) Optional Integer: Most trees dropped in one iteration; 0 or less removes the cap. Requires boosting = "dart". NULL uses LightGBM's default of 50.
skip_drop	(Tunable) Optional Numeric [0, 1]: Probability of skipping dropout for an iteration. Requires boosting = "dart". NULL uses LightGBM's default of 0.5.
uniform_drop	(Tunable) Optional Logical: Drop trees uniformly rather than weighted. Requires boosting = "dart". NULL uses LightGBM's default of FALSE.
xgboost_dart_mode	(Tunable) Optional Logical: Use the XGBoost DART normalization. Requires boosting = "dart". NULL uses LightGBM's default of FALSE.
drop_seed	Optional Integer: Random seed for choosing which trees to drop. Requires boosting = "dart". NULL uses LightGBM's default of 4.
top_rate	(Tunable) Optional Numeric [0, 1]: Share of large-gradient cases GOSS retains. Requires data_sample_strategy = "goss". NULL uses LightGBM's default of 0.2.
other_rate	(Tunable) Optional Numeric [0, 1]: Share of the remaining cases GOSS retains; with top_rate it must not exceed 1. Requires data_sample_strategy = "goss". NULL uses LightGBM's default of 0.1.
tweedie_variance_power	(Tunable) Optional Numeric [1, 2): Tweedie power: 1 is Poisson-like, near 2 is gamma-like. Requires objective = "tweedie". NULL uses LightGBM's default of 1.5.
fair_c	(Tunable) Optional Numeric (0, Inf): Fair loss scale. Requires objective = "fair". NULL uses LightGBM's default of 1.
poisson_max_delta_step	(Tunable) Optional Numeric (0, Inf): Step cap safeguarding Poisson optimization. Requires objective = "poisson". NULL uses LightGBM's default of 0.7.
sigmoid	(Tunable) Optional Numeric (0, Inf): Sigmoid slope, for a binary or one-vs-all objective. NULL uses LightGBM's default of 1.
boost_from_average	Optional Logical: Start boosting from the constant score that best fits the outcome under the objective, such as its mean for regression. NULL uses LightGBM's default of TRUE.
reg_sqrt	Optional Logical: Fit the square root of the outcome and square the prediction back, for the "regression", "regression_l1", "fair", "quantile" or "mape" objective. NULL uses LightGBM's default of FALSE.
ifw_lightgbm	(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in the LightGBM step.
alpha_glmnet	(Tunable) Numeric [0, 1]: Alpha for GLMNET.
lambda_glmnet	Optional Numeric [0, Inf) vector: Lambda for GLMNET. NULL = determined by cv.glmnet.
ifw_glmnet	(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in the GLMNET step.
ifw	Logical: If TRUE, use Inverse Frequency Weighting in classification. This applies IFW to both LightGBM and GLMNET; cannot be combined with ifw_lightgbm or ifw_glmnet.

Details

Get more information from lightgbm::lgb.train.

Value

LightRuleFitHyperparameters object.

Author(s)

EDG

Examples

lightrulefit_hyperparams <- setup_LightRuleFit(nrounds = 300L, max_depth = 3L)
lightrulefit_hyperparams

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