.. _`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).



**Documentation**

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.



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

*Output ports*:
    **Score** : table
        Score


**Definition**


*Input ports*

    :in-model:  model

        Input model

    :X:  table

        X

    :Y:  table

        Y


*Output ports*

    :Score:  table

        Score





.. automodule:: node_application

.. class:: Score