..
   Copyright 2021 IRT Saint-Exupéry, https://www.irt-saintexupery.com

   This work is licensed under the Creative Commons Attribution-ShareAlike 4.0
   International License. To view a copy of this license, visit
   http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative
   Commons, PO Box 1866, Mountain View, CA 94042, USA.

.. _gen_classification_algos:

Classification algorithms
=========================

.. warning::
    Some algorithms may require the :ref:`installation <installation>` of |g| with :ref:`all its features <dependencies>`
    and some others may depend on :ref:`plugins <plugins>`.

.. note::
    All the features of the wrapped optimization libraries may not be exposed through |g|.

.. raw:: html

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      cursor: pointer;
    }

    th, td {
      text-align: center;
    }

    tr:hover {
        border: 1px solid black;
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    tr:nth-child(even) {
      background-color: #f2f2f2
    }
    </style>

    <script>
    function sortTable(n) {
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      table = document.getElementById("myTable");
      switching = true;
      dir = "asc";
      while (switching) {
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        rows = table.rows;
        for (i = 1; i < (rows.length - 1); i++) {
          shouldSwitch = false;
          x = rows[i].getElementsByTagName("TD")[n];
          y = rows[i + 1].getElementsByTagName("TD")[n];
          if (dir == "asc") {
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              shouldSwitch= true;
              break;
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            if (x.innerHTML.toLowerCase() < y.innerHTML.toLowerCase()) {
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            switching = true;
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        }
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    </script>



.. _KNNClassifier_options:

KNNClassifier
-------------



Module: :class:`gemseo.mlearning.classification.knn`





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    <dl class="field-list simple">
    <dt class="field-odd">Required parameters</dt>
    <dd class="field-odd"><ul class="simple">

    <li>

**data** : *Dataset*

The learning dataset.

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    </li>

    </ul>
    </dd>
    </dl>



    <dl class="field-list simple">
    <dt class="field-odd">Optional parameters</dt>
    <dd class="field-odd"><ul class="simple">


    <li>

**input_names** : *Iterable[str] | None, optional*

The names of the input variables. If ``None``, consider all the input variables of the learning dataset.


By default it is set to None.


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    </li>

    <li>

**n_neighbors** : *int, optional*

The number of neighbors.


By default it is set to 5.


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    </li>

    <li>

**output_names** : *Iterable[str] | None, optional*

The names of the output variables. If ``None``, consider all the output variables of the learning dataset.


By default it is set to None.


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    </li>

    <li>

**transformer** : *Mapping[str, TransformerType] | None, optional*

The strategies to transform the variables. The values are instances of :class:`.Transformer` while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the :class:`.Transformer` will be applied to all the variables of this group. If None, do not transform the variables.


By default it is set to None.


.. raw:: html

    </li>

    <li>

****parameters** : *int | str*

The parameters of the machine learning algorithm.



.. raw:: html

    </li>

    </ul>
    </dd>
    </dl>



.. _RandomForestClassifier_options:

RandomForestClassifier
----------------------



Module: :class:`gemseo.mlearning.classification.random_forest`





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    <dl class="field-list simple">
    <dt class="field-odd">Required parameters</dt>
    <dd class="field-odd"><ul class="simple">

    <li>

**data** : *Dataset*

The learning dataset.

.. raw:: html

    </li>

    </ul>
    </dd>
    </dl>



    <dl class="field-list simple">
    <dt class="field-odd">Optional parameters</dt>
    <dd class="field-odd"><ul class="simple">


    <li>

**input_names** : *Iterable[str] | None, optional*

The names of the input variables. If ``None``, consider all the input variables of the learning dataset.


By default it is set to None.


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    </li>

    <li>

**n_estimators** : *int, optional*

The number of trees in the forest.


By default it is set to 100.


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    </li>

    <li>

**output_names** : *Iterable[str] | None, optional*

The names of the output variables. If ``None``, consider all the output variables of the learning dataset.


By default it is set to None.


.. raw:: html

    </li>

    <li>

**transformer** : *Mapping[str, TransformerType] | None, optional*

The strategies to transform the variables. The values are instances of :class:`.Transformer` while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the :class:`.Transformer` will be applied to all the variables of this group. If None, do not transform the variables.


By default it is set to None.


.. raw:: html

    </li>

    <li>

****parameters** : *int | float | bool | str | None*

The parameters of the machine learning algorithm.



.. raw:: html

    </li>

    </ul>
    </dd>
    </dl>



.. _SVMClassifier_options:

SVMClassifier
-------------



Module: :class:`gemseo.mlearning.classification.svm`





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    <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
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    <dl class="field-list simple">
    <dt class="field-odd">Required parameters</dt>
    <dd class="field-odd"><ul class="simple">

    <li>

**data** : *Dataset*

The learning dataset.

.. raw:: html

    </li>

    </ul>
    </dd>
    </dl>



    <dl class="field-list simple">
    <dt class="field-odd">Optional parameters</dt>
    <dd class="field-odd"><ul class="simple">


    <li>

**input_names** : *Iterable[str] | None, optional*

The names of the input variables. If ``None``, consider all the input variables of the learning dataset.


By default it is set to None.


.. raw:: html

    </li>

    <li>

**kernel** : *str | Callable | None, optional*

The name of the kernel or a callable for the SVM. Examples: "linear", "poly", "rbf", "sigmoid", "precomputed" or a callable.


By default it is set to rbf.


.. raw:: html

    </li>

    <li>

**output_names** : *Iterable[str] | None, optional*

The names of the output variables. If ``None``, consider all the output variables of the learning dataset.


By default it is set to None.


.. raw:: html

    </li>

    <li>

**probability** : *bool, optional*

Whether to enable the probability estimates. The algorithm is faster if set to False.


By default it is set to False.


.. raw:: html

    </li>

    <li>

**transformer** : *Mapping[str, TransformerType] | None, optional*

The strategies to transform the variables. The values are instances of :class:`.Transformer` while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the :class:`.Transformer` will be applied to all the variables of this group. If None, do not transform the variables.


By default it is set to None.


.. raw:: html

    </li>

    <li>

****parameters** : *int | float | bool | str | None*

The parameters of the machine learning algorithm.



.. raw:: html

    </li>

    </ul>
    </dd>
    </dl>