mlr_learners_regr.catboost: Gradient Boosted Decision Trees Regression Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Gradient Boosted Decision Trees Regression Learner

Description

Gradient boosting algorithm that also supports categorical data. Calls catboost::catboost.train() from package 'catboost'.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("regr.catboost")
lrn("regr.catboost")

Meta Information

• Task type: “regr”

• Predict Types: “response”

• Feature Types: “numeric”, “factor”, “ordered”

• Required Packages: mlr3, mlr3extralearners, catboost

Parameters

Id	Type	Default	Levels	Range
loss_function	character	RMSE	MAE, MAPE, Poisson, Quantile, RMSE, LogLinQuantile, Lq, Huber, Expectile, Tweedie	-
iterations	integer	1000		[1, \infty)
learning_rate	numeric	0.03		[0.001, 1]
random_seed	integer	0		[0, \infty)
l2_leaf_reg	numeric	3		[0, \infty)
bootstrap_type	character	-	Bayesian, Bernoulli, MVS, Poisson, No	-
bagging_temperature	numeric	1		[0, \infty)
subsample	numeric	-		[0, 1]
sampling_frequency	character	PerTreeLevel	PerTree, PerTreeLevel	-
sampling_unit	character	Object	Object, Group	-
mvs_reg	numeric	-		[0, \infty)
random_strength	numeric	1		[0, \infty)
depth	integer	6		[1, 16]
grow_policy	character	SymmetricTree	SymmetricTree, Depthwise, Lossguide	-
min_data_in_leaf	integer	1		[1, \infty)
max_leaves	integer	31		[1, \infty)
has_time	logical	FALSE	TRUE, FALSE	-
rsm	numeric	1		[0.001, 1]
nan_mode	character	Min	Min, Max	-
fold_permutation_block	integer	-		[1, 256]
leaf_estimation_method	character	-	Newton, Gradient, Exact	-
leaf_estimation_iterations	integer	-		[1, \infty)
leaf_estimation_backtracking	character	AnyImprovement	No, AnyImprovement, Armijo	-
fold_len_multiplier	numeric	2		[1.001, \infty)
approx_on_full_history	logical	TRUE	TRUE, FALSE	-
boosting_type	character	-	Ordered, Plain	-
boost_from_average	logical	-	TRUE, FALSE	-
langevin	logical	FALSE	TRUE, FALSE	-
diffusion_temperature	numeric	10000		[0, \infty)
score_function	character	Cosine	Cosine, L2, NewtonCosine, NewtonL2	-
monotone_constraints	untyped	-		-
feature_weights	untyped	-		-
first_feature_use_penalties	untyped	-		-
penalties_coefficient	numeric	1		[0, \infty)
per_object_feature_penalties	untyped	-		-
model_shrink_rate	numeric	-		(-\infty, \infty)
model_shrink_mode	character	-	Constant, Decreasing	-
target_border	numeric	-		(-\infty, \infty)
border_count	integer	-		[1, 65535]
feature_border_type	character	GreedyLogSum	Median, Uniform, UniformAndQuantiles, MaxLogSum, MinEntropy, GreedyLogSum	-
per_float_feature_quantization	untyped	-		-
thread_count	integer	1		[-1, \infty)
task_type	character	CPU	CPU, GPU	-
devices	untyped	-		-
logging_level	character	Silent	Silent, Verbose, Info, Debug	-
metric_period	integer	1		[1, \infty)
train_dir	untyped	"catboost_info"		-
model_size_reg	numeric	0.5		[0, 1]
allow_writing_files	logical	FALSE	TRUE, FALSE	-
save_snapshot	logical	FALSE	TRUE, FALSE	-
snapshot_file	untyped	-		-
snapshot_interval	integer	600		[1, \infty)
simple_ctr	untyped	-		-
combinations_ctr	untyped	-		-
ctr_target_border_count	integer	-		[1, 255]
counter_calc_method	character	Full	SkipTest, Full	-
max_ctr_complexity	integer	-		[1, \infty)
ctr_leaf_count_limit	integer	-		[1, \infty)
store_all_simple_ctr	logical	FALSE	TRUE, FALSE	-
final_ctr_computation_mode	character	Default	Default, Skip	-
verbose	logical	FALSE	TRUE, FALSE	-
ntree_start	integer	0		[0, \infty)
ntree_end	integer	0		[0, \infty)

Installation

See https://catboost.ai/en/docs/concepts/r-installation.

Initial parameter values

• logging_level:

  • Actual default: "Verbose"

  • Adjusted default: "Silent"

  • Reason for change: consistent with other mlr3 learners

• thread_count:

  • Actual default: -1

  • Adjusted default: 1

  • Reason for change: consistent with other mlr3 learners

• allow_writing_files:

  • Actual default: TRUE

  • Adjusted default: FALSE

  • Reason for change: consistent with other mlr3 learners

• save_snapshot:

  • Actual default: TRUE

  • Adjusted default: FALSE

  • Reason for change: consistent with other mlr3 learners

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCatboost

Methods

Public methods

• LearnerRegrCatboost$new()

• LearnerRegrCatboost$importance()

• LearnerRegrCatboost$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Create a LearnerRegrCatboost object.

Usage

LearnerRegrCatboost$new()

---

Method importance()

The importance scores are calculated using catboost.get_feature_importance, setting type = "FeatureImportance", returned for 'all'.

Usage

LearnerRegrCatboost$importance()

Returns

Named numeric().

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrCatboost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

sumny

References

Dorogush, Veronika A, Ershov, Vasily, Gulin, Andrey (2018). “CatBoost: gradient boosting with categorical features support.” arXiv preprint arXiv:1810.11363.

See Also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Examples

learner = mlr3::lrn("regr.catboost")
print(learner)

# available parameters:
learner$param_set$ids()

mlr-org/mlr3extralearners documentation built on April 13, 2024, 5:25 a.m.