GradientBoostingClassifier - b-cl - default - {‘zipmap’: False}#

Fitted on a problem type b-cl (see find_suitable_problem), method predict_proba matches output . Model was converted with additional parameter: <class 'sklearn.ensemble._gb.GradientBoostingClassifier'>={'zipmap': False}.

GradientBoostingClassifier(n_estimators=200, random_state=0)

index

0

skl_nop

201

skl_nnodes

554

skl_ntrees

200

skl_max_depth

3

onx_size

21641

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_ai.onnx.ml

1

onx_

15

onx_size_optim

21641

onx_nnodes_optim

1

onx_ninits_optim

0

fit_classes_.shape

2

fit_estimators_.shape

1

fit_train_score_.shape

200

fit_n_classes_

2

fit_n_features_

4

fit_estimators_.size

200

fit_estimators_.sum|.sum|tree_.leave_count

377

fit_estimators_.sum|.sum|tree_.node_count

554

fit_estimators_..n_features_

4

fit_estimators_.max|.max|tree_.max_depth

3
%0 X X float((0, 4)) TreeEnsembleClassifier TreeEnsembleClassifier (TreeEnsembleClassifier) base_values=[0.78845733] class_ids=[0 0 0 0 0 0 0 0 0 0 ... class_nodeids=[1 2 1 2 1 2 1 3 ... class_treeids=[  0   0   1   1 ... class_weights=[-3.19999993e-01 ... classlabels_int64s=[0 1] nodes_falsenodeids=[2 0 0 2 0 0... nodes_featureids=[2 0 0 2 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'LE... nodes_nodeids=[0 1 2 0 1 2 0 1 ... nodes_treeids=[  0   0   0   1 ... nodes_truenodeids=[1 0 0 1 0 0 ... nodes_values=[2.5489838 0.     ... post_transform=b'LOGISTIC' X->TreeEnsembleClassifier label label int64((0,)) probabilities probabilities float((0, 2)) TreeEnsembleClassifier->label TreeEnsembleClassifier->probabilities

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GradientBoostingClassifier - m-cl - default - {‘zipmap’: False}