Create an `mlr3` classification learner backed by the `catboost` R package.
Usage
make_catboost_learner(
iterations = 500L,
learning_rate = 0.03,
depth = 6L,
l2_leaf_reg = 5,
border_count = 64L,
patience = 40L,
validation_fraction = 0.15,
class_weights = "balanced",
device = c("auto", "cpu", "cuda"),
seed = 20260331L
)Arguments
- iterations
Maximum boosting iterations. Default: `500`.
- learning_rate
Learning rate. Default: `0.03`.
- depth
Tree depth. Default: `6`.
- l2_leaf_reg
L2 regularization on leaf values. Default: `5`.
- border_count
Number of numerical split candidates. Default: `64`.
- patience
Overfitting detector patience for the internal validation split. Default: `40`.
- validation_fraction
Fraction of the training fold reserved for early-stopping. Default: `0.15`.
- class_weights
Class weighting scheme. Use `"balanced"` (default), `"none"`, or a numeric vector with one weight per class.
- device
`"auto"` (default), `"cpu"`, or `"cuda"`.
- seed
Random seed used inside the backend. Default: `20260331`.