MultiOutputClassifier - ~m-label - logreg - {‘zipmap’: False}#

Fitted on a problem type ~m-label (see find_suitable_problem), method predict_proba matches output . Model was converted with additional parameter: <class 'sklearn.multioutput.MultiOutputClassifier'>={'zipmap': False}.

MultiOutputClassifier(estimator=LogisticRegression(random_state=0,
                                               solver='liblinear'),
                  n_jobs=8)

index

0

skl_nop

4

skl_ncoef

3

skl_nlin

3

onx_size

1652

onx_nnodes

11

onx_ninits

1

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_Reshape

3

onx_size_optim

1652

onx_nnodes_optim

11

onx_ninits_optim

1

fit_estimators_.size

3

fit_estimators_.intercept_.shape

1

fit_estimators_.classes_.shape

2

fit_estimators_.coef_.shape

(1, 4)

fit_estimators_.n_iter_.shape

1

%0 X X float((0, 4)) LinearClassifier1 LinearClassifier (LinearClassifier1) classlabels_ints=[0 1] coefficients=[-0.54714024  1.21... intercepts=[-0.84863716  0.8486... multi_class=1 post_transform=b'LOGISTIC' X->LinearClassifier1 LinearClassifier2 LinearClassifier (LinearClassifier2) classlabels_ints=[0 1] coefficients=[ 1.5634936  -0.16... intercepts=[ 0.951305 -0.951305... multi_class=1 post_transform=b'LOGISTIC' X->LinearClassifier2 LinearClassifier LinearClassifier (LinearClassifier) classlabels_ints=[0 1] coefficients=[-0.2891426 -0.935... intercepts=[ 0.6001405 -0.60014... multi_class=1 post_transform=b'LOGISTIC' X->LinearClassifier label label int64((0, 3)) probabilities probabilities [float()] Re_Reshapecst Re_Reshapecst int64((2,)) [-1  1] Re_Reshape1 Reshape (Re_Reshape1) allowzero=0 Re_Reshapecst->Re_Reshape1 Re_Reshape2 Reshape (Re_Reshape2) allowzero=0 Re_Reshapecst->Re_Reshape2 Re_Reshape Reshape (Re_Reshape) allowzero=0 Re_Reshapecst->Re_Reshape label2 label2 label2->Re_Reshape1 probability_tensor1 probability_tensor1 Normalizer1 Normalizer (Normalizer1) norm=b'L1' probability_tensor1->Normalizer1 LinearClassifier1->label2 LinearClassifier1->probability_tensor1 label3 label3 label3->Re_Reshape2 probability_tensor2 probability_tensor2 Normalizer2 Normalizer (Normalizer2) norm=b'L1' probability_tensor2->Normalizer2 LinearClassifier2->label3 LinearClassifier2->probability_tensor2 label1 label1 label1->Re_Reshape probability_tensor probability_tensor Normalizer Normalizer (Normalizer) norm=b'L1' probability_tensor->Normalizer LinearClassifier->label1 LinearClassifier->probability_tensor probabilities3 probabilities3 Se_SequenceConstruct SequenceConstruct (Se_SequenceConstruct) probabilities3->Se_SequenceConstruct Normalizer2->probabilities3 probabilities1 probabilities1 probabilities1->Se_SequenceConstruct Normalizer->probabilities1 probabilities2 probabilities2 probabilities2->Se_SequenceConstruct Normalizer1->probabilities2 Re_reshaped02 Re_reshaped02 Co_Concat Concat (Co_Concat) axis=1 Re_reshaped02->Co_Concat Re_Reshape1->Re_reshaped02 Re_reshaped03 Re_reshaped03 Re_reshaped03->Co_Concat Re_Reshape2->Re_reshaped03 Re_reshaped0 Re_reshaped0 Re_reshaped0->Co_Concat Re_Reshape->Re_reshaped0 Co_Concat->label Se_SequenceConstruct->probabilities