raful/sample_space
0
1��* �xgboost.sklearn��XGBRegressor���)��}�(�n_estimators�Kd� objective��reg:squarederror�� max_depth�K�2max_leaves�N�max_bin�N�grow_policy�N�
learning_rate�G?�������� verbosity�N�booster�N�tree_method�N�gamma�N�min_child_weight�N�max_delta_step�N� subsample�N�sampling_method�N�colsample_bytree�G?� �colsample_bylevel�N�colsample_bynode�N� reg_alpha�N�3reg_lambda�N�scale_pos_weight�N�4base_score�N�missing�G� �num_parallel_tree�N�random_state�N�n_jobs�N�monotone_constraints�N�interaction_constraints�N�importance_type�N�gpu_id�N�validate_parameters�N� predictor�N�enable_categorical���
feature_types�N�max_cat_to_onehot�N�max_cat_threshold�N�eval_metric�N�early_stopping_rounds�N� callbacks�N�_Booster��xgboost.core��Booster���)��}�(�handle��builtins�� bytearray���B� {L Config{L learner{L
generic_param{L fail_on_invalid_gpu_idSL 0L gpu_idSL -1L n_jobsSL 0L nthreadSL 0L random_stateSL 0L seedSL 0L seed_per_iterationSL 0L validate_parametersSL 1}L gradient_booster{L gbtree_model_param{L num_parallel_treeSL 1L num_treesSL 100L size_leaf_vectorSL 0}L gbtree_train_param{L predictorSL autoL process_typeSL defaultL tree_methodSL exactL updaterSL grow_colmaker,pruneL updater_seqSL grow_colmaker,prune}L nameSL gbtreeL specified_updaterFL updater{L
grow_colmaker{L colmaker_train_param{L default_directionSL learnL
opt_dense_colSL 1}L train_param{L alphaSL 0L cache_optSL 1L colsample_bylevelSL 1L colsample_bynodeSL 1L colsample_bytreeSL 0.5L etaSL 0.100000001L gammaSL 0L grow_policySL depthwiseL interaction_constraintsSL L lambdaSL 1L
learning_rateSL 0.100000001L max_binSL 256L max_cat_thresholdSL 64L max_cat_to_onehotSL 4L max_delta_stepSL 0L max_depthSL 3L 5max_leavesSL 0L min_child_weightSL 1L min_split_lossSL 0L monotone_constraintsSL ()L refresh_leafSL 1L reg_alphaSL 0L 6reg_lambdaSL 1L sampling_methodSL uniformL sketch_ratioSL 2L sparse_thresholdSL 0.20000000000000001L subsampleSL 1}}L prune{L train_param{L alphaSL 0L cache_optSL 1L colsample_bylevelSL 1L colsample_bynodeSL 1L colsample_bytreeSL 0.5L etaSL 0.100000001L gammaSL 0L grow_policySL depthwiseL interaction_constraintsSL L lambdaSL 1L
learning_rateSL 0.100000001L max_binSL 256L max_cat_thresholdSL 64L max_cat_to_onehotSL 4L max_delta_stepSL 0L max_depthSL 3L 7max_leavesSL 0L min_child_weightSL 1L min_split_lossSL 0L monotone_constraintsSL ()L refresh_leafSL 1L reg_alphaSL 0L 8reg_lambdaSL 1L sampling_methodSL uniformL sketch_ratioSL 2L sparse_thresholdSL 0.20000000000000001L subsampleSL 1}}}}L learner_model_param{L 9base_scoreSL 5E-1L boost_from_averageSL 1L num_classSL 0L num_featureSL 12L 10num_targetSL 1}L learner_train_param{L boosterSL gbtreeL disable_default_eval_metricSL 0L dsplitSL autoL objectiveSL reg:squarederror}L metrics[#L {L nameSL rmse}L objective{L nameSL reg:squarederrorL reg_loss_param{L scale_pos_weightSL 1}}}L version[#L iii}L Model{L learner{L 11attributes{L best_iterationSL 99L best_ntree_limitSL 100}L
feature_names[#L SL PercentageMalesSL MedianAgeSL LatitudeSL LongitudeSL PercentageHighSchoolGradsSL PercentagePHDsSL PercentageBelowPovertyLevelSL 12PopulationSL PercentageNoReligionSL PercentageEvangelicalsSL Lesbian_couplesSL gay_menL
feature_types[#L SL floatSL floatSL floatSL floatSL floatSL floatSL floatSL floatSL floatSL floatSL floatSL floatL gradient_booster{L model{L gbtree_model_param{L num_parallel_treeSL 1L num_treesSL 100L size_leaf_vectorSL 0}L tree_info[#L di i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i i L trees[#L d{L base_weights[$d#L B],�BF�pB��AB ��B_RB���A� B�8B�� B�T B$�$B���A| @P B� L 13categories[$l#L L categories_nodes[$l#L L categories_segments[$L#L L categories_sizes[$L#L L default_left[$U#L L idi L
left_children[$l#L
��������������������������������L loss_changes[$d#L FP��E��@E9 F*'�F,�DRV D'L L parents[$l#L ��� L right_children[$l#L 14