LogisticRegression - ~b-cl-64 - liblinear - {‘zipmap’: False}#

Fitted on a problem type ~b-cl-64 (see find_suitable_problem), method predict_proba matches output . Model was converted with additional parameter: <class 'sklearn.linear_model._logistic.LogisticRegression'>={'zipmap': False}.

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

index

0

skl_nop

1

skl_ncoef

1

skl_nlin

1

onx_size

1004

onx_nnodes

11

onx_ninits

5

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_

13

onx_ai.onnx.ml

1

onx_op_Cast

2

onx_op_Reshape

1

onx_size_optim

1004

onx_nnodes_optim

11

onx_ninits_optim

5

fit_classes_.shape

2

fit_coef_.shape

(1, 4)

fit_intercept_.shape

1

fit_n_iter_.shape

1
%0 X X double((0, 4)) MatMul MatMul (MatMul) X->MatMul label label int64((0,)) probabilities probabilities double((0, 2)) coef coef float64((4, 2)) [[ 0.45876741 -0.45876741] [ 1.29302622 -1.29302622] [-2.30693933  2.30693933] [-0.6970415   0.6970415 ]] coef->MatMul intercept intercept float64((1, 2)) [[ 0.28357965 -0.28357965]] Add Add (Add) intercept->Add classes classes int32((2,)) [0 1] ArrayFeatureExtractor ArrayFeatureExtractor (ArrayFeatureExtractor) classes->ArrayFeatureExtractor shape_tensor shape_tensor int64((1,)) [-1] Reshape Reshape (Reshape) shape_tensor->Reshape axis axis int64((1,)) [1] ReduceSum ReduceSum (ReduceSum) keepdims=1 axis->ReduceSum multiplied multiplied multiplied->Add MatMul->multiplied raw_scores raw_scores Sigmoid Sigmoid (Sigmoid) raw_scores->Sigmoid ArgMax ArgMax (ArgMax) axis=1 raw_scores->ArgMax Add->raw_scores raw_scoressig raw_scoressig Abs Abs (Abs) raw_scoressig->Abs NormalizerNorm Div (NormalizerNorm) raw_scoressig->NormalizerNorm Sigmoid->raw_scoressig label1 label1 label1->ArrayFeatureExtractor ArgMax->label1 array_feature_extractor_result array_feature_extractor_result Cast Cast (Cast) to=11 array_feature_extractor_result->Cast ArrayFeatureExtractor->array_feature_extractor_result norm_abs norm_abs norm_abs->ReduceSum Abs->norm_abs cast2_result cast2_result cast2_result->Reshape Cast->cast2_result norm norm norm->NormalizerNorm ReduceSum->norm reshaped_result reshaped_result Cast1 Cast (Cast1) to=7 reshaped_result->Cast1 Reshape->reshaped_result NormalizerNorm->probabilities Cast1->label

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LogisticRegression - ~b-cl-64 - liblinear - onnx/{‘zipmap’: False}