Classification Light GBM Learner

Calls lightgbm::lgb.train from package lightgbm.

Details

For categorical features either first transform factor colums with mlr3pipelines::PipeOpEncode or convert the columns with as.integer and specify the categorical columns with the categorical_feature parameter.

Dictionary

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

mlr_learners$get("classif.lightgbm")
lrn("classif.lightgbm")

Traits

  • Packages: lightgbm

  • Predict Types: prob, response

  • Feature Types: numeric, integer

  • Properties: importance, missings, multiclass, twoclass, weights

Custom mlr3 defaults

  • num_threads:

    • Actual default: 0L

    • Adjusted default: 1L

    • Reason for change: Prevents accidental conflicts with future.

  • verbose:

    • Actual default: 1L

    • Adjusted default: -1L

    • Reason for change: Prevents accidental conflicts with mlr messaging system.

See also

Dictionary of Learners: mlr3::mlr_learners

Author

kapsner

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLightGBM

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifLightGBM$new()

Method importance()

The importance scores are extracted from lbg.importance.

Usage

LearnerClassifLightGBM$importance()

Returns

Named numeric().

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifLightGBM$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.lightgbm"))
print(learner)

#> <LearnerClassifLightGBM:classif.lightgbm>
#> * Model: -
#> * Parameters: num_threads=1, verbose=-1
#> * Packages: lightgbm
#> * Predict Type: prob
#> * Feature types: numeric, integer
#> * Properties: importance, missings, multiclass, twoclass, weights

# available parameters:
learner$param_set$ids()

#>   [1] "nrounds"                       "objective"                    
#>   [3] "metric"                        "custom_eval"                  
#>   [5] "verbose"                       "record"                       
#>   [7] "eval_freq"                     "init_model"                   
#>   [9] "early_stopping_rounds"         "callbacks"                    
#>  [11] "reset_data"                    "categorical_feature"          
#>  [13] "boosting"                      "linear_tree"                  
#>  [15] "num_iterations"                "learning_rate"                
#>  [17] "num_leaves"                    "tree_learner"                 
#>  [19] "num_threads"                   "device_type"                  
#>  [21] "seed"                          "deterministic"                
#>  [23] "force_col_wise"                "force_row_wise"               
#>  [25] "histogram_pool_size"           "max_depth"                    
#>  [27] "min_data_in_leaf"              "min_sum_hessian_in_leaf"      
#>  [29] "bagging_fraction"              "pos_bagging_fraction"         
#>  [31] "neg_bagging_fraction"          "bagging_freq"                 
#>  [33] "bagging_seed"                  "feature_fraction"             
#>  [35] "feature_fraction_bynode"       "feature_fraction_seed"        
#>  [37] "extra_trees"                   "extra_seed"                   
#>  [39] "first_metric_only"             "max_delta_step"               
#>  [41] "lambda_l1"                     "lambda_l2"                    
#>  [43] "linear_lambda"                 "min_gain_to_split"            
#>  [45] "drop_rate"                     "max_drop"                     
#>  [47] "skip_drop"                     "xgboost_dart_mode"            
#>  [49] "uniform_drop"                  "drop_seed"                    
#>  [51] "top_rate"                      "other_rate"                   
#>  [53] "min_data_per_group"            "max_cat_threshold"            
#>  [55] "cat_l2"                        "cat_smooth"                   
#>  [57] "max_cat_to_onehot"             "top_k"                        
#>  [59] "monotone_constraints"          "monotone_constraints_method"  
#>  [61] "monotone_penalty"              "feature_contri"               
#>  [63] "forcedsplits_filename"         "refit_decay_rate"             
#>  [65] "cegb_tradeoff"                 "cegb_penalty_split"           
#>  [67] "cegb_penalty_feature_lazy"     "cegb_penalty_feature_coupled" 
#>  [69] "path_smooth"                   "interaction_constraints"      
#>  [71] "input_model"                   "output_model"                 
#>  [73] "saved_feature_importance_type" "snapshot_freq"                
#>  [75] "max_bin"                       "max_bin_by_feature"           
#>  [77] "min_data_in_bin"               "bin_construct_sample_cnt"     
#>  [79] "data_random_seed"              "is_enable_sparse"             
#>  [81] "enable_bundle"                 "use_missing"                  
#>  [83] "zero_as_missing"               "feature_pre_filter"           
#>  [85] "pre_partition"                 "two_round"                    
#>  [87] "header"                        "group_column"                 
#>  [89] "forcedbins_filename"           "save_binary"                  
#>  [91] "objective_seed"                "is_unbalance"                 
#>  [93] "scale_pos_weight"              "sigmoid"                      
#>  [95] "boost_from_average"            "lambdarank_truncation_level"  
#>  [97] "lambdarank_norm"               "label_gain"                   
#>  [99] "metric_freq"                   "is_provide_training_metric"   
#> [101] "eval_at"                       "multi_error_top_k"            
#> [103] "auc_mu_weights"                "num_machines"                 
#> [105] "local_listen_port"             "time_out"                     
#> [107] "machine_list_filename"         "machines"                     
#> [109] "gpu_platform_id"               "gpu_device_id"                
#> [111] "gpu_use_dp"                    "num_gpu"                      
#> [113] "start_iteration"               "num_iteration"                
#> [115] "pred_early_stop"               "pred_early_stop_freq"         
#> [117] "pred_early_stop_margin"        "output_result"