Setup hyperparameters for LightRuleFit training.
Usage
setup_LightRuleFit(
nrounds = 200L,
num_leaves = 32L,
max_depth = 4L,
learning_rate = 0.1,
subsample = 0.666,
subsample_freq = 1L,
lambda_l1 = 0,
lambda_l2 = 0,
objective = NULL,
ifw_lightgbm = FALSE,
alpha = 1,
lambda = NULL,
ifw_glmnet = FALSE,
ifw = FALSE
)Arguments
- nrounds
(Tunable) Positive integer: Number of boosting rounds.
- num_leaves
(Tunable) Positive integer: Maximum number of leaves in one tree.
- max_depth
(Tunable) Integer: Maximum depth of trees.
- learning_rate
(Tunable) Numeric: Learning rate.
- subsample
(Tunable) Numeric: Fraction of data to use.
- subsample_freq
(Tunable) Positive integer: Frequency of subsample.
- lambda_l1
(Tunable) Numeric: L1 regularization.
- lambda_l2
(Tunable) Numeric: L2 regularization.
- objective
Character: Objective function.
- ifw_lightgbm
(Tunable) Logical: If TRUE, use Inverse Frequency Weighting in the LightGBM step.
- alpha
(Tunable) Numeric: Alpha for GLMNET.
- lambda
Numeric: Lambda for 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.
Details
Get more information from lightgbm::lgb.train.