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

Fitted on a problem type ~b-cl-nan (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

33161

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

33161

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

492

fit__predictors.sum|tree_.node_count

884

fit__predictors.max|tree_.max_depth

4

%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 4 5 6 1 3 5 6 ... class_treeids=[ 0  0  0  0  1  ... class_weights=[-3.19999993e-01 ... classlabels_int64s=[0 1] nodes_falsenodeids=[2 0 6 5 0 0... nodes_featureids=[2 0 1 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'LE... nodes_nodeids=[0 1 2 3 4 5 6 0 ... nodes_treeids=[ 0  0  0  0  0  ... nodes_truenodeids=[1 0 3 4 0 0 ... nodes_values=[2.548984   0.    ... post_transform=b'LOGISTIC' X->TreeEnsembleClassifier label label int64((0,)) probabilities probabilities float((0, 2)) TreeEnsembleClassifier->label TreeEnsembleClassifier->probabilities