LogisticRegression - m-cl - liblinear -#

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

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

index

0

skl_nop

1

skl_ncoef

3

skl_nlin

1

onx_size

681

onx_nnodes

4

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_

9

onx_op_Cast

1

onx_op_ZipMap

1

onx_size_optim

681

onx_nnodes_optim

4

onx_ninits_optim

0

fit_classes_.shape

3

fit_coef_.shape

(3, 4)

fit_intercept_.shape

3

fit_n_iter_.shape

1

%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 output_label output_label int64((0,)) output_probability output_probability [{int64, {'kind': 'tensor', 'elem': 'float', 'shape': }}] label label Cast Cast (Cast) to=7 label->Cast probability_tensor probability_tensor Normalizer Normalizer (Normalizer) norm=b'L1' probability_tensor->Normalizer LinearClassifier->label LinearClassifier->probability_tensor probabilities probabilities ZipMap ZipMap (ZipMap) classlabels_int64s=[0 1 2] probabilities->ZipMap Normalizer->probabilities Cast->output_label ZipMap->output_probability