DecisionTreeClassifier - ~b-cl-f100 - default - {‘zipmap’: False}#

Fitted on a problem type ~b-cl-f100 (see find_suitable_problem), method predict_proba matches output . Model was converted with additional parameter: <class 'sklearn.tree._classes.DecisionTreeClassifier'>={'zipmap': False}.

DecisionTreeClassifier(random_state=0)

index

0

skl_nop

1

skl_nnodes

3

skl_ntrees

1

skl_max_depth

1

onx_size

707

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

707

onx_nnodes_optim

1

onx_ninits_optim

0

fit_classes_.shape

2

fit_n_classes_

2

fit_n_features_

100

fit_tree_.node_count

3

fit_tree_.leave_count

2

fit_tree_.max_depth

1
%0 X X float((0, 100)) TreeEnsembleClassifier TreeEnsembleClassifier (TreeEnsembleClassifier) class_ids=[0 0] class_nodeids=[1 2] class_treeids=[0 0] class_weights=[0. 1.] classlabels_int64s=[0 1] nodes_falsenodeids=[2 0 0] nodes_featureids=[78  0  0] nodes_hitrates=[1. 1. 1.] nodes_missing_value_tracks_true=[0 0 0] nodes_modes=[b'BRANCH_LEQ' b'LE... nodes_nodeids=[0 1 2] nodes_treeids=[0 0 0] nodes_truenodeids=[1 0 0] nodes_values=[2.4957602 0.     ... post_transform=b'NONE' X->TreeEnsembleClassifier label label int64((0,)) probabilities probabilities float((0, 2)) TreeEnsembleClassifier->label TreeEnsembleClassifier->probabilities

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

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DecisionTreeClassifier - ~m-label - default - {‘zipmap’: False}