NuSVC - m-cl - prob -#

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

NuSVC(probability=True, random_state=0)

index

0

skl_nop

1

onx_size

3199

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

2

onx_op_ZipMap

1

onx_size_optim

3199

onx_nnodes_optim

4

onx_ninits_optim

0

fit_class_weight_.shape

3

fit_classes_.shape

3

fit_support_.shape

84

fit_support_vectors_.shape

(84, 4)

fit_dual_coef_.shape

(2, 84)

fit_intercept_.shape

3

%0 X X float((0, 4)) SVMc SVMClassifier (SVMc) classlabels_ints=[0 1 2] coefficients=[ 0.16048989  0.16... kernel_params=[0.06311981 0.   ... kernel_type=b'RBF' post_transform=b'NONE' prob_a=[-3.9767365 -3.7842703 -... prob_b=[ 0.3082117   0.11605771... rho=[-0.00929845 -0.0250503  -0... support_vectors=[ 5.4688959e+00... vectors_per_class=[22 37 25] X->SVMc output_label output_label int64((0,)) output_probability output_probability [{int64, {'kind': 'tensor', 'elem': 'float', 'shape': }}] label label Cast1 Cast (Cast1) to=7 label->Cast1 SVM02 SVM02 Cast Cast (Cast) to=1 SVM02->Cast SVMc->label SVMc->SVM02 probabilities probabilities ZipMap ZipMap (ZipMap) classlabels_int64s=[0 1 2] probabilities->ZipMap Cast->probabilities Cast1->output_label ZipMap->output_probability