Gradient Boosted Decision Trees Classification Learner

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("classif.catboost")
lrn("classif.catboost")

Traits

  • Packages: catboost

  • Predict Types: response, prob

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

  • Properties: importance, missings, multiclass, twoclass, 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

Dictionary of Learners: mlr3::mlr_learners

Author

sumny

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifCatboost

Methods

Public methods

Inherited methods

Method new()

Create a LearnerClassifCatboost object.

Usage

LearnerClassifCatboost$new()

Method importance()

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

Usage

LearnerClassifCatboost$importance()

Returns

Named numeric().

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifCatboost$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("classif.catboost"))
print(learner)

#> <LearnerClassifCatboost:classif.catboost>
#> * Model: -
#> * Parameters: loss_function_twoclass=Logloss,
#>   loss_function_multiclass=MultiClass, 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, multiclass, twoclass, weights

# available parameters:
learner$param_set$ids()

#>  [1] "loss_function_twoclass"         "loss_function_multiclass"      
#>  [3] "iterations"                     "learning_rate"                 
#>  [5] "random_seed"                    "l2_leaf_reg"                   
#>  [7] "bootstrap_type"                 "bagging_temperature"           
#>  [9] "subsample"                      "sampling_frequency"            
#> [11] "sampling_unit"                  "mvs_reg"                       
#> [13] "random_strength"                "depth"                         
#> [15] "grow_policy"                    "min_data_in_leaf"              
#> [17] "max_leaves"                     "has_time"                      
#> [19] "rsm"                            "nan_mode"                      
#> [21] "fold_permutation_block"         "leaf_estimation_method"        
#> [23] "leaf_estimation_iterations"     "leaf_estimation_backtracking"  
#> [25] "fold_len_multiplier"            "approx_on_full_history"        
#> [27] "class_weights"                  "auto_class_weights"            
#> [29] "boosting_type"                  "boost_from_average"            
#> [31] "langevin"                       "diffusion_temperature"         
#> [33] "score_function"                 "monotone_constraints"          
#> [35] "feature_weights"                "first_feature_use_penalties"   
#> [37] "penalties_coefficient"          "per_object_feature_penalties"  
#> [39] "model_shrink_rate"              "model_shrink_mode"             
#> [41] "target_border"                  "border_count"                  
#> [43] "feature_border_type"            "per_float_feature_quantization"
#> [45] "classes_count"                  "thread_count"                  
#> [47] "task_type"                      "devices"                       
#> [49] "logging_level"                  "metric_period"                 
#> [51] "train_dir"                      "model_size_reg"                
#> [53] "allow_writing_files"            "save_snapshot"                 
#> [55] "snapshot_file"                  "snapshot_interval"             
#> [57] "simple_ctr"                     "combinations_ctr"              
#> [59] "ctr_target_border_count"        "counter_calc_method"           
#> [61] "max_ctr_complexity"             "ctr_leaf_count_limit"          
#> [63] "store_all_simple_ctr"           "final_ctr_computation_mode"    
#> [65] "verbose"                        "ntree_start"                   
#> [67] "ntree_end"