LGBMClassifier - b-cl - default -#

Fitted on a problem type b-cl (see find_suitable_problem), method predict_proba matches output .

LGBMClassifier(n_jobs=8, random_state=0)

index

0

skl_nop

1

onx_size

36141

onx_nnodes

3

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_

14

onx_op_Cast

1

onx_op_ZipMap

1

onx_size_optim

36141

onx_nnodes_optim

3

onx_ninits_optim

0

fit_n_classes_

2

fit_n_features_

4

fit_objective

binary sigmoid:1

fit_n_classes

1

fit_estimators_.size

100

fit_node_count

962

fit_ntrees

100

fit_leave_count

531

fit_mode_count

2

%0 X X float((0, 4)) LgbmClassifier TreeEnsembleClassifier (LgbmClassifier) class_ids=[0 0 0 0 0 0 0 0 0 0 ... class_nodeids=[ 1  2  1  3  4  ... class_treeids=[ 0  0  1  1  1  ... class_weights=[ 4.81386662e-01 ... classlabels_int64s=[0 1] nodes_falsenodeids=[ 2  0  0  2... nodes_featureids=[2 0 0 2 0 0 0... nodes_hitrates=[1. 1. 1. 1. 1. ... nodes_missing_value_tracks_true=[1 0 0 1 0... nodes_modes=[b'BRANCH_LEQ' b'LE... nodes_nodeids=[ 0  1  2  0  1  ... nodes_treeids=[ 0  0  0  1  1  ... nodes_truenodeids=[ 1  0  0  1 ... nodes_values=[3.106216   0.    ... post_transform=b'LOGISTIC' X->LgbmClassifier output_label output_label int64((0,)) output_probability output_probability [{int64, {'kind': 'tensor', 'elem': 'float', 'shape': }}] label_tensor label_tensor Cast Cast (Cast) to=7 label_tensor->Cast probability_tensor probability_tensor ZipMap ZipMap (ZipMap) classlabels_int64s=[0 1] probability_tensor->ZipMap LgbmClassifier->label_tensor LgbmClassifier->probability_tensor ZipMap->output_probability Cast->output_label