BaggingRegressor - b-reg - default -#

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

BaggingRegressor(n_jobs=8, random_state=0)

index

0

skl_nop

11

skl_nnodes

1388

skl_ntrees

10

skl_max_depth

14

onx_size

56327

onx_nnodes

22

onx_ninits

1

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_Reshape

10

onx_size_optim

56327

onx_nnodes_optim

22

onx_ninits_optim

1

fit_n_features_

4

fit_estimators_.size

10

fit_estimators_.sum|tree_.node_count

1388

fit_estimators_.sum|tree_.leave_count

699

fit_estimators_.n_features_

4

fit_estimators_.max|tree_.max_depth

14
%0 X X float((0, 4)) TreeEnsembleRegressor1 TreeEnsembleRegressor (TreeEnsembleRegressor1) n_targets=1 nodes_falsenodeids=[ 40  13   6... nodes_featureids=[2 3 2 3 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 ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[  1   2   3 ... nodes_values=[2.4172986  0.1834... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0 0 0... target_nodeids=[  4   5   7   9... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.   0.04 0.02 ... X->TreeEnsembleRegressor1 TreeEnsembleRegressor6 TreeEnsembleRegressor (TreeEnsembleRegressor6) n_targets=1 nodes_falsenodeids=[ 38  23   4... nodes_featureids=[2 1 1 0 1 0 3... 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 ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[  1   2   3 ... nodes_values=[2.532492   3.5203... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0 0 0... target_nodeids=[  3   5  10  12... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.38 0.01 0.13 ... X->TreeEnsembleRegressor6 TreeEnsembleRegressor2 TreeEnsembleRegressor (TreeEnsembleRegressor2) n_targets=1 nodes_falsenodeids=[ 36  11  10... nodes_featureids=[2 2 0 3 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 ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[  1   2   3 ... nodes_values=[2.4079013  1.0568... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0 0 0... target_nodeids=[  5   6   8   9... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.04 0.   0.13 ... X->TreeEnsembleRegressor2 TreeEnsembleRegressor5 TreeEnsembleRegressor (TreeEnsembleRegressor5) n_targets=1 nodes_falsenodeids=[ 52  35  30... nodes_featureids=[2 2 3 1 0 0 2... 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 ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[  1   2   3 ... nodes_values=[2.7056267  1.5517... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0 0 0... target_nodeids=[  4   7  10  11... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.41 0.13 0.08 ... X->TreeEnsembleRegressor5 TreeEnsembleRegressor TreeEnsembleRegressor (TreeEnsembleRegressor) n_targets=1 nodes_falsenodeids=[ 46  33  30... nodes_featureids=[2 2 2 0 2 0 3... 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 ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[  1   2   3 ... nodes_values=[2.7556252  1.5517... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0 0 0... target_nodeids=[  5   7   8  14... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.13 0.04 0.03 ... X->TreeEnsembleRegressor TreeEnsembleRegressor9 TreeEnsembleRegressor (TreeEnsembleRegressor9) n_targets=1 nodes_falsenodeids=[ 46   7   4... nodes_featureids=[2 2 1 0 2 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 ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[  1   2   3 ... nodes_values=[2.6641772  1.0219... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0 0 0... target_nodeids=[  3   5   6   9... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.13 0.   0.04 ... X->TreeEnsembleRegressor9 TreeEnsembleRegressor8 TreeEnsembleRegressor (TreeEnsembleRegressor8) n_targets=1 nodes_falsenodeids=[ 50  41   8... nodes_featureids=[2 2 2 1 1 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 ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[  1   2   3 ... nodes_values=[ 2.5489838   1.72... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0 0 0... target_nodeids=[  5   6   7  11... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.13 0.   0.39 ... X->TreeEnsembleRegressor8 TreeEnsembleRegressor3 TreeEnsembleRegressor (TreeEnsembleRegressor3) n_targets=1 nodes_falsenodeids=[ 40   7   4... nodes_featureids=[2 0 2 0 2 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 ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[  1   2   3 ... nodes_values=[2.4861827  4.4261... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0 0 0... target_nodeids=[  3   5   6  10... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.13 0.04 0.03 ... X->TreeEnsembleRegressor3 TreeEnsembleRegressor4 TreeEnsembleRegressor (TreeEnsembleRegressor4) n_targets=1 nodes_falsenodeids=[ 36  31   6... nodes_featureids=[2 2 1 0 0 0 1... 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 ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[  1   2   3 ... nodes_values=[2.5489838  1.7306... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0 0 0... target_nodeids=[  4   5  10  11... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.41 0.38 0.13 ... X->TreeEnsembleRegressor4 TreeEnsembleRegressor7 TreeEnsembleRegressor (TreeEnsembleRegressor7) n_targets=1 nodes_falsenodeids=[ 42  39  12... nodes_featureids=[2 3 1 1 3 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 ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[  1   2   3 ... nodes_values=[ 2.5489838   0.78... post_transform=b'NONE' target_ids=[0 0 0 0 0 0 0 0 0 0... target_nodeids=[  6   7   8  10... target_treeids=[0 0 0 0 0 0 0 0... target_weights=[0.41 0.38 0.34 ... X->TreeEnsembleRegressor7 variable variable float((0, 1)) shape_tensor shape_tensor int64((3,)) [ 1 -1  1] Reshape2 Reshape (Reshape2) shape_tensor->Reshape2 Reshape3 Reshape (Reshape3) shape_tensor->Reshape3 Reshape Reshape (Reshape) shape_tensor->Reshape Reshape1 Reshape (Reshape1) shape_tensor->Reshape1 Reshape5 Reshape (Reshape5) shape_tensor->Reshape5 Reshape6 Reshape (Reshape6) shape_tensor->Reshape6 Reshape7 Reshape (Reshape7) shape_tensor->Reshape7 Reshape8 Reshape (Reshape8) shape_tensor->Reshape8 Reshape4 Reshape (Reshape4) shape_tensor->Reshape4 Reshape9 Reshape (Reshape9) shape_tensor->Reshape9 variable_1 variable_1 variable_1->Reshape1 TreeEnsembleRegressor1->variable_1 variable_6 variable_6 variable_6->Reshape6 TreeEnsembleRegressor6->variable_6 variable_2 variable_2 variable_2->Reshape2 TreeEnsembleRegressor2->variable_2 variable_5 variable_5 variable_5->Reshape5 TreeEnsembleRegressor5->variable_5 variable_0 variable_0 variable_0->Reshape TreeEnsembleRegressor->variable_0 variable_9 variable_9 variable_9->Reshape9 TreeEnsembleRegressor9->variable_9 variable_8 variable_8 variable_8->Reshape8 TreeEnsembleRegressor8->variable_8 variable_3 variable_3 variable_3->Reshape3 TreeEnsembleRegressor3->variable_3 variable_4 variable_4 variable_4->Reshape4 TreeEnsembleRegressor4->variable_4 variable_7 variable_7 variable_7->Reshape7 TreeEnsembleRegressor7->variable_7 reshaped_proba2 reshaped_proba2 Concat Concat (Concat) axis=0 reshaped_proba2->Concat Reshape2->reshaped_proba2 reshaped_proba3 reshaped_proba3 reshaped_proba3->Concat Reshape3->reshaped_proba3 reshaped_proba reshaped_proba reshaped_proba->Concat Reshape->reshaped_proba reshaped_proba1 reshaped_proba1 reshaped_proba1->Concat Reshape1->reshaped_proba1 reshaped_proba5 reshaped_proba5 reshaped_proba5->Concat Reshape5->reshaped_proba5 reshaped_proba6 reshaped_proba6 reshaped_proba6->Concat Reshape6->reshaped_proba6 reshaped_proba7 reshaped_proba7 reshaped_proba7->Concat Reshape7->reshaped_proba7 reshaped_proba8 reshaped_proba8 reshaped_proba8->Concat Reshape8->reshaped_proba8 reshaped_proba4 reshaped_proba4 reshaped_proba4->Concat Reshape4->reshaped_proba4 reshaped_proba9 reshaped_proba9 reshaped_proba9->Concat Reshape9->reshaped_proba9 merged_proba merged_proba ReduceMean ReduceMean (ReduceMean) axes=[0] keepdims=0 merged_proba->ReduceMean Concat->merged_proba ReduceMean->variable

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