.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/mlearning/classification_model/plot_random_forest_classification.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_random_forest_classification.py>`
        to download the full example code

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

.. _sphx_glr_examples_mlearning_classification_model_plot_random_forest_classification.py:


Random forest classification
============================

We want to classify the Iris dataset using a Random Forest classifier.

.. GENERATED FROM PYTHON SOURCE LINES 28-30

Import
------

.. GENERATED FROM PYTHON SOURCE LINES 30-40

.. code-block:: default

    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo import create_benchmark_dataset
    from gemseo.mlearning import create_classification_model
    from numpy import array

    configure_logger()






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

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 41-43

Load Iris dataset
-----------------

.. GENERATED FROM PYTHON SOURCE LINES 43-45

.. code-block:: default

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








.. GENERATED FROM PYTHON SOURCE LINES 46-50

Create the classification model
-------------------------------
Then, we build the linear regression model from the discipline cache and
displays this model.

.. GENERATED FROM PYTHON SOURCE LINES 50-54

.. code-block:: default

    model = create_classification_model("RandomForestClassifier", data=iris)
    model.learn()
    print(model)





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

 .. code-block:: none

    RandomForestClassifier(n_estimators=100)
       based on the scikit-learn library
       built from 150 learning samples




.. GENERATED FROM PYTHON SOURCE LINES 55-58

Predict output
--------------
Once it is built, we can use it for prediction.

.. GENERATED FROM PYTHON SOURCE LINES 58-66

.. code-block:: default

    input_value = {
        "sepal_length": array([4.5]),
        "sepal_width": array([3.0]),
        "petal_length": array([1.0]),
        "petal_width": array([0.2]),
    }
    output_value = model.predict(input_value)
    print(output_value)




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

 .. code-block:: none

    {'specy': array([0])}





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

   **Total running time of the script:** ( 0 minutes  0.201 seconds)


.. _sphx_glr_download_examples_mlearning_classification_model_plot_random_forest_classification.py:

.. only:: html

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




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

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

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

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


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_