.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/mlearning/classification_model/plot_classification_api.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_mlearning_classification_model_plot_classification_api.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_mlearning_classification_model_plot_classification_api.py:


High-level functions
====================

The :mod:`gemseo.mlearning` package includes high-level functions
to create classification models from model class names.

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.. code-block:: Python


    from __future__ import annotations

    from gemseo import create_benchmark_dataset
    from gemseo.mlearning import create_classification_model
    from gemseo.mlearning import get_classification_models
    from gemseo.mlearning import get_classification_options








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Available models
----------------
Use the :func:`.get_classification_models`
to list the available model class names:

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.. code-block:: Python

    get_classification_models()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    ['KNNClassifier', 'RandomForestClassifier', 'SVMClassifier']



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Available model options
-----------------------
Use the :func:`.get_classification_options`
to get the options of a model
from its class name:

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.. code-block:: Python

    get_classification_options("KNNClassifier", pretty_print=False)





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 .. code-block:: none


    {'additionalProperties': False, 'description': 'The settings of the k-nearest neighbors classification algorithm.', 'properties': {'transformer': {'additionalProperties': True, 'description': 'The strategies to transform the variables.\n\nThe values are instances of :class:`.BaseTransformer`\nwhile the keys are the names of\neither the variables\nor the groups of variables,\ne.g. ``"inputs"`` or ``"outputs"``\nin the case of the regression algorithms.\nIf a group is specified,\nthe :class:`.BaseTransformer` will be applied\nto all the variables of this group.\nIf :attr:`.IDENTITY`, do not transform the variables.', 'title': 'Transformer', 'type': 'object'}, 'parameters': {'additionalProperties': True, 'description': 'Other parameters.', 'title': 'Parameters', 'type': 'object'}, 'input_names': {'default': [], 'description': 'The names of the input variables', 'items': {'type': 'string'}, 'title': 'Input Names', 'type': 'array'}, 'output_names': {'default': [], 'description': 'The names of the output variables', 'items': {'type': 'string'}, 'title': 'Output Names', 'type': 'array'}, 'n_neighbors': {'default': 5, 'description': 'The number of neighbors.', 'exclusiveMinimum': 0, 'title': 'N Neighbors', 'type': 'integer'}}, 'title': 'KNNClassifier_Settings', 'type': 'object'}



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.. seealso::
   The functions
   :func:`.get_classification_models` and :func:`.get_classification_options`
   can be very useful for the developers.
   As a user,
   it may be easier to consult :ref:`this page <gen_classification_algos>`
   to find out about the different algorithms and their options.

Creation
--------
Given a training dataset, *e.g.*

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.. code-block:: Python

    dataset = create_benchmark_dataset("IrisDataset", as_io=True)







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use the :func:`.create_classification_model` function
to create a classification model from its class name and settings:

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.. code-block:: Python

    model = create_classification_model("KNNClassifier", data=dataset)
    model.learn()








.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.020 seconds)


.. _sphx_glr_download_examples_mlearning_classification_model_plot_classification_api.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_classification_api.ipynb <plot_classification_api.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_classification_api.py <plot_classification_api.py>`

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      :download:`Download zipped: plot_classification_api.zip <plot_classification_api.zip>`


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