.. _l-LassoLars-m-reg-default--o17:

LassoLars - m-reg - default - 
==============================

Fitted on a problem type *m-reg*
(see :func:`find_suitable_problem
<mlprodict.onnxrt.validate.validate_problems.find_suitable_problem>`),
method `predict` matches output .


::

        LassoLars(random_state=0)

+----------------------+----------+
| index                | 0        |
+======================+==========+
| skl_nop              | 1        |
+----------------------+----------+
| skl_ncoef            | 2        |
+----------------------+----------+
| skl_nlin             | 1        |
+----------------------+----------+
| onx_size             | 294      |
+----------------------+----------+
| onx_nnodes           | 1        |
+----------------------+----------+
| 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 | 1.13.1   |
+----------------------+----------+
| onx_ai.onnx.ml       | 1        |
+----------------------+----------+
| onx_                 | 17       |
+----------------------+----------+
| onx_size_optim       | 294      |
+----------------------+----------+
| onx_nnodes_optim     | 1        |
+----------------------+----------+
| onx_ninits_optim     | 0        |
+----------------------+----------+
| fit_coef_.shape      | (2, 4)   |
+----------------------+----------+
| fit_intercept_.shape | 2        |
+----------------------+----------+


.. gdot::

        digraph{
      size=7;
      ranksep=0.25;
      nodesep=0.05;
      orientation=portrait;

      X [shape=box color=red label="X\nfloat((0, 4))" fontsize=10];

      variable [shape=box color=green label="variable\nfloat((0, 2))" fontsize=10];


      LinearRegressor [shape=box style="filled,rounded" color=orange label="LinearRegressor\n(LinearRegressor)\ncoefficients=[0.         0.    ...\nintercepts=[0.61977136 1.119771...\ntargets=2" fontsize=10];
      X -> LinearRegressor;
      LinearRegressor -> variable;
    }