StackingRegressor - b-reg - linreg -#

Fitted on a problem type b-reg (see find_suitable_problem), method predict matches output .

StackingRegressor(estimators=[('lr1', LinearRegression()),
                          ('lr2', LinearRegression(fit_intercept=False))],
              n_jobs=8)

index

0

skl_nop

3

skl_ncoef

8

skl_nlin

2

onx_size

842

onx_nnodes

8

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_

14

onx_op_Cast

3

onx_op_Identity

1

onx_size_optim

782

onx_nnodes_optim

7

onx_ninits_optim

0

fit_estimators_.size

2

fit_estimators_.singular_.shape

4

fit_estimators_.coef_.shape

4
%0 X X float((0, 4)) LinearRegressor LinearRegressor (LinearRegressor) coefficients=[-0.19476996  0.04... intercepts=[0.17583406] X->LinearRegressor LinearRegressor1 LinearRegressor (LinearRegressor1) coefficients=[-0.16883491  0.06... intercepts=[0.] X->LinearRegressor1 variable variable float((0, 1)) variable1 variable1 Cast Cast (Cast) to=1 variable1->Cast LinearRegressor->variable1 variable2 variable2 Cast1 Cast (Cast1) to=1 variable2->Cast1 LinearRegressor1->variable2 variable2_castio variable2_castio Concat Concat (Concat) axis=1 variable2_castio->Concat Cast1->variable2_castio variable1_castio variable1_castio variable1_castio->Concat Cast->variable1_castio merged_probability_tensor merged_probability_tensor LinearRegressor2 LinearRegressor (LinearRegressor2) coefficients=[0.09499621 0.8980... intercepts=[0.01630316] merged_probability_tensor->LinearRegressor2 Concat->merged_probability_tensor variable3 variable3 Cast2 Cast (Cast2) to=1 variable3->Cast2 LinearRegressor2->variable3 variable3_castio variable3_castio Identity Identity (Identity) variable3_castio->Identity Cast2->variable3_castio Identity->variable

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StackingClassifier - b-cl - logreg - {‘zipmap’: False}

next

HistGradientBoostingClassifier - b-cl - default - {‘zipmap’: False}