.. _`Score`:

.. _`org.sysess.sympathy.machinelearning.score`:

Score
~~~~~

.. image:: score.svg
   :width: 48


Scores the model using given X and Y data. Exact semantics
depends on the type of model (classifier, regressor, etc).

:Configuration:


  - *default method*

    Uses the default scoring method defined by the used model.
    Semantics of the scoring depend on the type of node

    (classifier, regressor, etc). Otherwise the problem is assumed
    to be a classification problem, a single predict call is
    made and extended information is given for each target.
    If model does not implement the predict function then a
    transform is used instead.



:Inputs:
    **in-model** : model
        Input model
    **X** : table
        X
    **Y** : table
        Y

:Outputs:
    **Score** : table
        Score

*Ports*:

    **Inputs**:

        :in-model: model

            Input model
        :X: table

            X
        :Y: table

            Y

    **Outputs**:

        :Score: table

            Score

*Configuration*:

    **default method**
        Uses the default scoring method defined by the used model.
        Semantics of the scoring depend on the type of node

        (classifier, regressor, etc). Otherwise the problem is assumed
        to be a classification problem, a single predict call is
        made and extended information is given for each target.
        If model does not implement the predict function then a
        transform is used instead.

.. automodule:: node_application

.. class:: Score