GaussianProcessRegressor - b-reg - dotproduct - cdist#
Fitted on a problem type b-reg
(see find_suitable_problem),
method predict matches output .
Model was converted with additional parameter: <class 'sklearn.gaussian_process._gpr.GaussianProcessRegressor'>={'optim': 'cdist'}.
GaussianProcessRegressor(alpha=100.0, kernel=DotProduct(sigma_0=1),
random_state=0)
index |
0 |
|---|---|
skl_nop |
1 |
onx_size |
2784 |
onx_nnodes |
5 |
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 |
1.13.1 |
16 |
|
onx_op_Reshape |
1 |
onx_size_optim |
2784 |
onx_nnodes_optim |
5 |
onx_ninits_optim |
5 |
fit_X_train_.shape |
(112, 4) |
fit_y_train_.shape |
112 |
fit_L_.shape |
(112, 112) |
fit_alpha_.shape |
112 |