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")

Traits

  • Packages: catboost

  • Predict Types: response

  • Feature Types: logical, integer, numeric, factor, ordered

  • Properties: importance, missings, weights

Custom mlr3 defaults

  • 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

References

CatBoost: unbiased boosting with categorical features. Liudmila Prokhorenkova, Gleb Guse, Aleksandr Vorobev, Anna Veronika Dorogush and Andrey Gulin. 2017. https://arxiv.org/abs/1706.09516.

CatBoost: gradient boosting with categorical features support. Anna Veronika Dorogush, Vasily Ershov and Andrey Gulin. 2018. https://arxiv.org/abs/1810.11363.

See also

Author

sumny

Super classes

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

Methods

Public methods

Inherited methods

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.

Examples

# stop example failing with warning if package not installed learner = suppressWarnings(mlr3::lrn("regr.catboost")) print(learner)
#> <LearnerRegrCatboost:regr.catboost> #> * Model: - #> * Parameters: loss_function=RMSE, logging_level=Silent, thread_count=1, #> allow_writing_files=FALSE, save_snapshot=FALSE #> * Packages: catboost #> * Predict Type: response #> * Feature types: logical, integer, numeric, factor, ordered #> * Properties: importance, missings, weights
# available parameters: learner$param_set$ids()
#> [1] "loss_function" "iterations" #> [3] "learning_rate" "random_seed" #> [5] "l2_leaf_reg" "bootstrap_type" #> [7] "bagging_temperature" "subsample" #> [9] "sampling_frequency" "sampling_unit" #> [11] "mvs_reg" "random_strength" #> [13] "depth" "grow_policy" #> [15] "min_data_in_leaf" "max_leaves" #> [17] "has_time" "rsm" #> [19] "nan_mode" "fold_permutation_block" #> [21] "leaf_estimation_method" "leaf_estimation_iterations" #> [23] "leaf_estimation_backtracking" "fold_len_multiplier" #> [25] "approx_on_full_history" "boosting_type" #> [27] "boost_from_average" "langevin" #> [29] "diffusion_temperature" "score_function" #> [31] "monotone_constraints" "feature_weights" #> [33] "first_feature_use_penalties" "penalties_coefficient" #> [35] "per_object_feature_penalties" "model_shrink_rate" #> [37] "model_shrink_mode" "target_border" #> [39] "border_count" "feature_border_type" #> [41] "per_float_feature_quantization" "thread_count" #> [43] "task_type" "devices" #> [45] "logging_level" "metric_period" #> [47] "train_dir" "model_size_reg" #> [49] "allow_writing_files" "save_snapshot" #> [51] "snapshot_file" "snapshot_interval" #> [53] "simple_ctr" "combinations_ctr" #> [55] "ctr_target_border_count" "counter_calc_method" #> [57] "max_ctr_complexity" "ctr_leaf_count_limit" #> [59] "store_all_simple_ctr" "final_ctr_computation_mode" #> [61] "verbose" "ntree_start" #> [63] "ntree_end"