.. _`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).
Typically, for classifiers the score will be the mean accuracy
(fraction of correct predictions out of all predictions).





Definition
::::::::::


Input ports
...........

    **in-model**
        | Type: model
        | Description: Input model
    **X**
        | Type: table
        | Description: X
    **Y**
        | Type: table
        | Description: Y

Output ports
............

    **Score**
        | Type: table
        | Description: Score



Configuration
.............

    **Use built-in default scoring** (default method)
        Use the default scoring method defined by the used model.
        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.







Examples
........





The node can be found in:

* :download:`Custom_ParameterSearch.syx </Examples/Sympathy for Data/MachineLearning/Custom_ParameterSearch.syx>`

* :download:`Text_Classification.syx </Examples/Sympathy for Data/MachineLearning/Text_Classification.syx>`

* :download:`Eigenfaces.syx </Examples/Sympathy for Data/MachineLearning/Eigenfaces.syx>`

* :download:`Polynomial_Features.syx </Examples/Sympathy for Data/MachineLearning/Polynomial_Features.syx>`

* :download:`Custom_CrossValidation.syx </Examples/Sympathy for Data/MachineLearning/Custom_CrossValidation.syx>`

* :download:`Fisher_faces.syx </Examples/Advanced Machine Learning/Fisher_faces.syx>`




Implementation
..............

.. automodule:: node_application
    :noindex:

.. class:: Score
    :noindex: