VotingClassifier - m-cl - logreg-noflatten - {‘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.ensemble._voting.VotingClassifier'>={'zipmap': False}.

VotingClassifier(estimators=[('lr1', LogisticRegression(solver='liblinear')),
                         ('lr2',
                          LogisticRegression(fit_intercept=False,
                                             solver='liblinear'))],
             flatten_transform=False, n_jobs=8, voting='soft')

index

0

skl_nop

3

skl_ncoef

6

skl_nlin

2

onx_size

1498

onx_nnodes

12

onx_ninits

4

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_

15

onx_ai.onnx.ml

1

onx_op_Cast

2

onx_op_Reshape

1

onx_size_optim

1472

onx_nnodes_optim

12

onx_ninits_optim

3

fit_classes_.shape

3

fit_estimators_.size

2

fit_estimators_.intercept_.shape

3

fit_estimators_.coef_.shape

(3, 4)

fit_estimators_.n_iter_.shape

1

fit_estimators_.classes_.shape

3
%0 X X float((0, 4)) LinearClassifier LinearClassifier (LinearClassifier) classlabels_ints=[0 1 2] coefficients=[ 0.45876738  1.29... intercepts=[ 0.28357968  0.8646... multi_class=1 post_transform=b'LOGISTIC' X->LinearClassifier LinearClassifier1 LinearClassifier (LinearClassifier1) classlabels_ints=[0 1 2] coefficients=[ 0.49765062  1.31... intercepts=[0. 0. 0.] multi_class=1 post_transform=b'LOGISTIC' X->LinearClassifier1 label label int64((0,)) probabilities probabilities float((0, 3)) ArgMax ArgMax (ArgMax) axis=1 probabilities->ArgMax classes_ind classes_ind int64((1, 3)) [[0 1 2]] w0 w0 float32((1,)) [0.5] Mul1 Mul (Mul1) w0->Mul1 Mul Mul (Mul) w0->Mul classes classes int32((3,)) [0 1 2] ArrayFeatureExtractor ArrayFeatureExtractor (ArrayFeatureExtractor) classes->ArrayFeatureExtractor shape_tensor shape_tensor int64((1,)) [-1] Reshape Reshape (Reshape) shape_tensor->Reshape label_0 label_0 probability_tensor probability_tensor Normalizer Normalizer (Normalizer) norm=b'L1' probability_tensor->Normalizer LinearClassifier->label_0 LinearClassifier->probability_tensor label_1 label_1 probability_tensor1 probability_tensor1 Normalizer1 Normalizer (Normalizer1) norm=b'L1' probability_tensor1->Normalizer1 LinearClassifier1->label_1 LinearClassifier1->probability_tensor1 voting_proba_0 voting_proba_0 voting_proba_0->Mul Normalizer->voting_proba_0 voting_proba_1 voting_proba_1 voting_proba_1->Mul1 Normalizer1->voting_proba_1 wprob_name1 wprob_name1 Sum Sum (Sum) wprob_name1->Sum Mul1->wprob_name1 wprob_name wprob_name wprob_name->Sum Mul->wprob_name Sum->probabilities label_name label_name label_name->ArrayFeatureExtractor ArgMax->label_name array_feature_extractor_result array_feature_extractor_result Cast Cast (Cast) to=1 array_feature_extractor_result->Cast ArrayFeatureExtractor->array_feature_extractor_result cast2_result cast2_result cast2_result->Reshape Cast->cast2_result reshaped_result reshaped_result Cast1 Cast (Cast1) to=7 reshaped_result->Cast1 Reshape->reshaped_result Cast1->label

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