HistGradientBoostingClassifier - ~b-cl-64 - default - {‘zipmap’: False}#

Fitted on a problem type ~b-cl-64 (see find_suitable_problem), method predict_proba matches output . Model was converted with additional parameter: <class 'sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier'>={'zipmap': False}.

HistGradientBoostingClassifier(random_state=0)

index

0

skl_nop

1

onx_size

12437

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

12437

onx_nnodes_optim

1

onx_ninits_optim

0

fit_classes_.shape

2

fit_train_score_.shape

0

fit_validation_score_.shape

0

fit__predictors.size

100

fit__predictors.sum|tree_.leave_count

208

fit__predictors.sum|tree_.node_count

316

fit__predictors.max|tree_.max_depth

2
%0 X X double((0, 4)) TreeEnsembleClassifierDouble TreeEnsembleClassifierDouble (TreeEnsembleClassifierDouble) base_values=[0.78845733] class_ids=[0 0 0 0 0 0 0 0 0 0 ... class_nodeids=[1 2 1 3 4 1 3 4 ... class_treeids=[ 0  0  1  1  1  ... class_weights=[-0.32        0.1... classlabels_int64s=[0 1] nodes_falsenodeids=[2 0 0 2 0 4... nodes_featureids=[2 0 0 2 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'LE... nodes_nodeids=[0 1 2 0 1 2 3 4 ... nodes_treeids=[ 0  0  0  1  1  ... nodes_truenodeids=[1 0 0 1 0 3 ... nodes_values=[2.548984  0.     ... post_transform=b'LOGISTIC' X->TreeEnsembleClassifierDouble label label int64((0,)) probabilities probabilities double((0, 2)) TreeEnsembleClassifierDouble->label TreeEnsembleClassifierDouble->probabilities

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

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HistGradientBoostingClassifier - ~b-cl-nan - default - {‘zipmap’: False}