ExtraTreesClassifier - 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._forest.ExtraTreesClassifier'>={'zipmap': False}.

ExtraTreesClassifier(n_estimators=10, n_jobs=8, random_state=0)

index

0

skl_nop

11

skl_nnodes

172

skl_ntrees

10

skl_max_depth

8

onx_size

6909

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

6909

onx_nnodes_optim

1

onx_ninits_optim

0

fit_classes_.shape

2

fit_n_classes_

2

fit_n_features_

4

fit_estimators_.size

10

fit_estimators_.n_classes_

2

fit_estimators_.sum|tree_.node_count

172

fit_estimators_.classes_.shape

2

fit_estimators_.sum|tree_.leave_count

91

fit_estimators_.n_features_

4

fit_estimators_.max|tree_.max_depth

8
%0 X X float((0, 4)) TreeEnsembleClassifier TreeEnsembleClassifier (TreeEnsembleClassifier) class_ids=[0 0 0 0 0 0 0 0 0 0 ... class_nodeids=[ 3  4  7 10 11 1... class_treeids=[0 0 0 0 0 0 0 0 ... class_weights=[0.  0.1 0.  0.  ... classlabels_int64s=[0 1] nodes_falsenodeids=[14  5  4  0... nodes_featureids=[0 1 2 0 0 3 3... nodes_hitrates=[1. 1. 1. 1. 1. ... nodes_missing_value_tracks_true=[0 0 0 0 0... nodes_modes=[b'BRANCH_LEQ' b'BR... nodes_nodeids=[ 0  1  2  3  4  ... nodes_treeids=[0 0 0 0 0 0 0 0 ... nodes_truenodeids=[ 1  2  3  0 ... nodes_values=[5.5576315  2.4811... post_transform=b'NONE' X->TreeEnsembleClassifier label label int64((0,)) probabilities probabilities float((0, 2)) TreeEnsembleClassifier->label TreeEnsembleClassifier->probabilities

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