모델 설정

enable_constant_model

enable_decision_tree

enable_glm

enable_xgboost_gbm

enable_lightgbm

enable_xgboost_dart

enable_xgboost_rapids

enable_xgboost_rf

enable_xgboost_gbm_dask

enable_xgboost_dart_dask

enable_lightgbm_dask

다중 노드 Dask를 활성화하려면, Dask 다중 노드 학습 을 참조하십시오.

enable_hyperopt_dask

num_inner_hyperopt_trials_prefinal

num_inner_hyperopt_trials_final

num_hyperopt_individuals_final

optuna_pruner

optuna_sampler

enable_xgboost_hyperopt_callback

enable_lightgbm_hyperopt_callback

enable_tensorflow

enable_grownet

enable_ftrl

enable_rulefit

enable_zero_inflated_models

enable_lightgbm_boosting_types

enable_lightgbm_cat_support

enable_lightgbm_cuda_support

show_constant_model

params_tensorflow

max_nestimators

n_estimators_list_no_early_stopping

min_learning_rate_final

max_learning_rate_final

max_nestimators_feature_evolution_factor

max_abs_score_delta_train_valid

max_rel_score_delta_train_valid

min_learning_rate

max_learning_rate

max_epochs

max_max_depth

max_max_bin

rulefit_max_num_rules

ensemble_meta_learner

fixed_ensemble_level

cross_validate_meta_learner

cross_validate_single_final_model

parameter_tuning_num_models

imbalance_sampling_method

imbalance_sampling_threshold_min_rows_original

imbalance_ratio_sampling_threshold

heavy_imbalance_ratio_sampling_threshold

imbalance_sampling_number_of_bags

imbalance_sampling_max_number_of_bags

imbalance_sampling_max_number_of_bags_feature_evolution

imbalance_sampling_max_multiple_data_size

imbalance_sampling_target_minority_fraction

ftrl_max_interaction_terms_per_degree

enable_bootstrap

tensorflow_num_classes_switch

prediction_intervals

prediction_intervals_alpha

dump_modelparams_every_scored_indiv