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

Fitted on a problem type m-cl (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

21

skl_ntrees

1

skl_max_depth

6

onx_size

1610

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

1610

onx_nnodes_optim

1

onx_ninits_optim

0

fit_classes_.shape

3

fit_n_classes_

3

fit_n_features_

4

fit_tree_.node_count

21

fit_tree_.leave_count

11

fit_tree_.max_depth

6
%0 X X float((0, 4)) TreeEnsembleClassifier TreeEnsembleClassifier (TreeEnsembleClassifier) class_ids=[0 1 2 0 1 2 0 1 2 0 ... class_nodeids=[ 1  1  1  5  5  ... class_treeids=[0 0 0 0 0 0 0 0 ... class_weights=[1. 0. 0. 0. 1. 0... classlabels_int64s=[0 1 2] nodes_falsenodeids=[ 2  0 12 11... nodes_featureids=[2 0 2 3 3 0 3... 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  ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[ 1  0  3  4 ... nodes_values=[2.5489838 0.     ... post_transform=b'NONE' X->TreeEnsembleClassifier label label int64((0,)) probabilities probabilities float((0, 3)) TreeEnsembleClassifier->label TreeEnsembleClassifier->probabilities

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

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