AdaBoostRegressor - b-reg - default -#

Fitted on a problem type b-reg (see find_suitable_problem), method predict matches output .

AdaBoostRegressor(n_estimators=10, random_state=0)

index

0

skl_nop

11

skl_nnodes

146

skl_ntrees

10

skl_max_depth

3

onx_size

10903

onx_nnodes

23

onx_ninits

7

onx_doc_string

onx_ir_version

8

onx_domain

ai.onnx

onx_model_version

0

onx_producer_name

skl2onnx

onx_producer_version

1.11.1

onx_

15

onx_ai.onnx.ml

1

onx_op_Cast

1

onx_op_Reshape

1

onx_size_optim

10903

onx_nnodes_optim

23

onx_ninits_optim

7

fit_estimator_weights_.shape

10

fit_estimator_errors_.shape

10

fit_estimators_.size

10

fit_estimators_.sum|tree_.node_count

146

fit_estimators_.sum|tree_.leave_count

78

fit_estimators_.n_features_

4

fit_estimators_.max|tree_.max_depth

3
%0 X X float((0, 4)) TreeEnsembleRegressor4 TreeEnsembleRegressor (TreeEnsembleRegressor4) n_targets=1 nodes_falsenodeids=[ 8  5  4  0... nodes_featureids=[3 1 2 0 0 0 0... nodes_hitrates=[1. 1. 1. 1. 1. ... nodes_missing_value_tracks_true=[0 0 0 0 0... nodes_modes=[b'BRANCH_LEQ' b'BR... nodes_nodeids=[ 0  1  2  3  4  ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[ 1  2  3  0 ... nodes_values=[1.1216526 2.97020... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0] target_nodeids=[ 3  4  6  7 10 ... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[1.93       1.75... X->TreeEnsembleRegressor4 TreeEnsembleRegressor1 TreeEnsembleRegressor (TreeEnsembleRegressor1) n_targets=1 nodes_falsenodeids=[ 8  5  4  0... nodes_featureids=[2 2 2 0 0 0 0... nodes_hitrates=[1. 1. 1. 1. 1. ... nodes_missing_value_tracks_true=[0 0 0 0 0... nodes_modes=[b'BRANCH_LEQ' b'BR... nodes_nodeids=[ 0  1  2  3  4  ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[ 1  2  3  0 ... nodes_values=[2.5489838 1.52457... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0] target_nodeids=[ 3  4  6  7 10 ... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.39999998 0.16... X->TreeEnsembleRegressor1 TreeEnsembleRegressor8 TreeEnsembleRegressor (TreeEnsembleRegressor8) n_targets=1 nodes_falsenodeids=[ 8  5  4  0... nodes_featureids=[2 0 3 0 0 2 0... nodes_hitrates=[1. 1. 1. 1. 1. ... nodes_missing_value_tracks_true=[0 0 0 0 0... nodes_modes=[b'BRANCH_LEQ' b'BR... nodes_nodeids=[ 0  1  2  3  4  ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[ 1  2  3  0 ... nodes_values=[2.5489838 4.87456... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0] target_nodeids=[ 3  4  6  7 10 ... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.01666667 0.03... X->TreeEnsembleRegressor8 TreeEnsembleRegressor6 TreeEnsembleRegressor (TreeEnsembleRegressor6) n_targets=1 nodes_falsenodeids=[ 8  5  4  0... nodes_featureids=[2 2 1 0 0 2 0... nodes_hitrates=[1. 1. 1. 1. 1. ... nodes_missing_value_tracks_true=[0 0 0 0 0... nodes_modes=[b'BRANCH_LEQ' b'BR... nodes_nodeids=[ 0  1  2  3  4  ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[ 1  2  3  0 ... nodes_values=[2.4861827 1.58091... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0] target_nodeids=[ 3  4  6  7 10 ... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.41       0.09... X->TreeEnsembleRegressor6 TreeEnsembleRegressor7 TreeEnsembleRegressor (TreeEnsembleRegressor7) n_targets=1 nodes_falsenodeids=[ 8  5  4  0... nodes_featureids=[2 0 0 0 0 0 0... nodes_hitrates=[1. 1. 1. 1. 1. ... nodes_missing_value_tracks_true=[0 0 0 0 0... nodes_modes=[b'BRANCH_LEQ' b'BR... nodes_nodeids=[ 0  1  2  3  4  ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[ 1  2  3  0 ... nodes_values=[2.5489838 4.42612... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0] target_nodeids=[ 3  4  6  7 10 ... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.04       0.03... X->TreeEnsembleRegressor7 TreeEnsembleRegressor3 TreeEnsembleRegressor (TreeEnsembleRegressor3) n_targets=1 nodes_falsenodeids=[ 8  5  4  0... nodes_featureids=[2 3 2 0 0 0 0... nodes_hitrates=[1. 1. 1. 1. 1. ... nodes_missing_value_tracks_true=[0 0 0 0 0... nodes_modes=[b'BRANCH_LEQ' b'BR... nodes_nodeids=[ 0  1  2  3  4  ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[ 1  2  3  0 ... nodes_values=[2.5489838  0.4727... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0] target_nodeids=[ 3  4  6  7 10 ... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.02666667 0.19... X->TreeEnsembleRegressor3 TreeEnsembleRegressor2 TreeEnsembleRegressor (TreeEnsembleRegressor2) n_targets=1 nodes_falsenodeids=[ 8  5  4  0... nodes_featureids=[2 1 2 0 0 1 0... nodes_hitrates=[1. 1. 1. 1. 1. ... nodes_missing_value_tracks_true=[0 0 0 0 0... nodes_modes=[b'BRANCH_LEQ' b'BR... nodes_nodeids=[ 0  1  2  3  4  ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[ 1  2  3  0 ... nodes_values=[2.4079013 3.05844... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0] target_nodeids=[ 3  4  6  7 10 ... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.13       0.05... X->TreeEnsembleRegressor2 TreeEnsembleRegressor9 TreeEnsembleRegressor (TreeEnsembleRegressor9) n_targets=1 nodes_falsenodeids=[ 6  3  0  5... nodes_featureids=[2 0 0 0 0 0 0... nodes_hitrates=[1. 1. 1. 1. 1. ... nodes_missing_value_tracks_true=[0 0 0 0 0... nodes_modes=[b'BRANCH_LEQ' b'BR... nodes_nodeids=[ 0  1  2  3  4  ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[ 1  2  0  4 ... nodes_values=[2.5645442 4.46456... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0] target_nodeids=[ 2  4  5  8  9 ... target_treeids=[0 0 0 0 0 0 0] target_weights=[0.04      0.44 ... X->TreeEnsembleRegressor9 TreeEnsembleRegressor5 TreeEnsembleRegressor (TreeEnsembleRegressor5) n_targets=1 nodes_falsenodeids=[ 8  5  4  0... nodes_featureids=[2 2 1 0 0 3 0... nodes_hitrates=[1. 1. 1. 1. 1. ... nodes_missing_value_tracks_true=[0 0 0 0 0... nodes_modes=[b'BRANCH_LEQ' b'BR... nodes_nodeids=[ 0  1  2  3  4  ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[ 1  2  3  0 ... nodes_values=[4.816925  2.81091... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0] target_nodeids=[ 3  4  6  7  9 ... target_treeids=[0 0 0 0 0 0 0] target_weights=[0.2       0.465... X->TreeEnsembleRegressor5 TreeEnsembleRegressor TreeEnsembleRegressor (TreeEnsembleRegressor) n_targets=1 nodes_falsenodeids=[ 8  5  4  0... nodes_featureids=[2 0 2 0 0 1 0... nodes_hitrates=[1. 1. 1. 1. 1. ... nodes_missing_value_tracks_true=[0 0 0 0 0... nodes_modes=[b'BRANCH_LEQ' b'BR... nodes_nodeids=[ 0  1  2  3  4  ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[ 1  2  3  0 ... nodes_values=[2.6928241 5.41864... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0] target_nodeids=[ 3  4  6  7 10 ... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.0375     0.34... X->TreeEnsembleRegressor variable variable float((0, 1)) negate negate float32(()) -1.0 Mul Mul (Mul) negate->Mul estimators_weights estimators_weights float32((10,)) [1.4365486  1.5002506  1.3294014  0.54848987 1.219... ArrayFeatureExtractor ArrayFeatureExtractor (ArrayFeatureExtractor) estimators_weights->ArrayFeatureExtractor half_scalar half_scalar float32(()) 0.5 Mul1 Mul (Mul1) half_scalar->Mul1 last_index last_index int64(()) 9 ArrayFeatureExtractor1 ArrayFeatureExtractor (ArrayFeatureExtractor1) last_index->ArrayFeatureExtractor1 k_value k_value int64((1,)) [10] TopK1 TopK (TopK1) k_value->TopK1 shape_tensor shape_tensor int64((2,)) [-1 10] Reshape Reshape (Reshape) shape_tensor->Reshape axis_name axis_name int32(()) 1 CumSum CumSum (CumSum) axis_name->CumSum est_label_4 est_label_4 Concat Concat (Concat) axis=1 est_label_4->Concat TreeEnsembleRegressor4->est_label_4 est_label_1 est_label_1 est_label_1->Concat TreeEnsembleRegressor1->est_label_1 est_label_8 est_label_8 est_label_8->Concat TreeEnsembleRegressor8->est_label_8 est_label_6 est_label_6 est_label_6->Concat TreeEnsembleRegressor6->est_label_6 est_label_7 est_label_7 est_label_7->Concat TreeEnsembleRegressor7->est_label_7 est_label_3 est_label_3 est_label_3->Concat TreeEnsembleRegressor3->est_label_3 est_label_2 est_label_2 est_label_2->Concat TreeEnsembleRegressor2->est_label_2 est_label_9 est_label_9 est_label_9->Concat TreeEnsembleRegressor9->est_label_9 est_label_5 est_label_5 est_label_5->Concat TreeEnsembleRegressor5->est_label_5 est_label_0 est_label_0 est_label_0->Concat TreeEnsembleRegressor->est_label_0 concatenated_labels concatenated_labels concatenated_labels->Mul GatElsB GatherElements (GatElsB) axis=1 concatenated_labels->GatElsB Concat->concatenated_labels negated_labels negated_labels negated_labels->TopK1 Mul->negated_labels sorted_values sorted_values sorted_indices sorted_indices sorted_indices->ArrayFeatureExtractor GatElsA GatherElements (GatElsA) axis=1 sorted_indices->GatElsA TopK1->sorted_values TopK1->sorted_indices array_feat_extractor_output array_feat_extractor_output array_feat_extractor_output->Reshape ArrayFeatureExtractor->array_feat_extractor_output reshaped_weights reshaped_weights reshaped_weights->CumSum Reshape->reshaped_weights weights_cdf weights_cdf weights_cdf->ArrayFeatureExtractor1 Less Less (Less) weights_cdf->Less CumSum->weights_cdf median_value median_value median_value->Mul1 ArrayFeatureExtractor1->median_value comp_value comp_value comp_value->Less Mul1->comp_value median_or_above median_or_above Cast Cast (Cast) to=1 median_or_above->Cast Less->median_or_above cast_result cast_result ArgMin ArgMin (ArgMin) axis=1 cast_result->ArgMin Cast->cast_result median_idx median_idx median_idx->GatElsA ArgMin->median_idx median_estimators median_estimators median_estimators->GatElsB GatElsA->median_estimators GatElsB->variable

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