GradientBoostingRegressor - ~b-reg-64 - default -#

Fitted on a problem type ~b-reg-64 (see find_suitable_problem), method predict matches output .

GradientBoostingRegressor(n_estimators=200, random_state=0)

index

0

skl_nop

201

skl_nnodes

2766

skl_ntrees

200

skl_max_depth

3

onx_size

124739

onx_nnodes

1

onx_ninits

0

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_mlprodict

1

onx_

15

onx_size_optim

124739

onx_nnodes_optim

1

onx_ninits_optim

0

fit_estimators_.shape

1

fit_train_score_.shape

200

fit_n_classes_

1

fit_n_features_

4

fit_estimators_.size

200

fit_estimators_.sum|.sum|tree_.leave_count

1483

fit_estimators_.sum|.sum|tree_.node_count

2766

fit_estimators_..n_features_

4

fit_estimators_.max|.max|tree_.max_depth

3

%0 X X double((0, 4)) TreeEnsembleRegressorDouble TreeEnsembleRegressorDouble (TreeEnsembleRegressorDouble) base_values=[1.78866071] n_targets=1 nodes_falsenodeids=[ 8  5  4 ..... nodes_featureids=[2 0 0 ... 0 0... nodes_hitrates=[1. 1. 1. ... 1.... nodes_missing_value_tracks_true=[0 0 0 ...... nodes_modes=[b'BRANCH_LEQ' b'BR... nodes_nodeids=[ 0  1  2 ... 10 ... nodes_treeids=[  0   0   0 ... ... nodes_truenodeids=[ 1  2  3 ...... nodes_values=[2.54898393 4.4532... post_transform=b'NONE' target_ids=[0 0 0 ... 0 0 0] target_nodeids=[ 3  4  6 ...  9... target_treeids=[  0   0   0 ...... target_weights=[-0.16586607 -0.... X->TreeEnsembleRegressorDouble variable variable double((0, 1)) TreeEnsembleRegressorDouble->variable