LinearDiscriminantAnalysis - m-cl - default -#

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

LinearDiscriminantAnalysis()

index

0

skl_nop

1

skl_ncoef

3

skl_nlin

1

onx_size

595

onx_nnodes

3

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

595

onx_nnodes_optim

3

onx_ninits_optim

0

fit_classes_.shape

3

fit_priors_.shape

3

fit_means_.shape

(3, 4)

fit_xbar_.shape

4

fit_explained_variance_ratio_.shape

2

fit_scalings_.shape

(4, 2)

fit_intercept_.shape

3

fit_coef_.shape

(3, 4)

%0 X X float((0, 4)) LinearClassifier LinearClassifier (LinearClassifier) classlabels_ints=[0 1 2] coefficients=[ 2.610924    3.72... intercepts=[  2.4400733  -1.598... multi_class=0 post_transform=b'SOFTMAX' 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 probabilities probabilities ZipMap ZipMap (ZipMap) classlabels_int64s=[0 1 2] probabilities->ZipMap LinearClassifier->label LinearClassifier->probabilities ZipMap->output_probability Cast->output_label