.. 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 ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_mlearning_classification_model_plot_classification_api.py: Classification API ================== Here are some examples of the machine learning API applied to classification models. .. GENERATED FROM PYTHON SOURCE LINES 28-40 .. code-block:: Python from __future__ import annotations from gemseo import configure_logger 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 configure_logger() .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 41-43 Get available classification models ----------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 43-45 .. code-block:: Python get_classification_models() .. rst-class:: sphx-glr-script-out .. code-block:: none ['KNNClassifier', 'RandomForestClassifier', 'SVMClassifier'] .. GENERATED FROM PYTHON SOURCE LINES 46-48 Get classification model options -------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 48-50 .. code-block:: Python get_classification_options("KNNClassifier") .. rst-class:: sphx-glr-script-out .. code-block:: none +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ | Name | Description | Type | +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ | input_names | The names of the input variables. if ``none``, consider all the input variables of the | null | | | learning dataset. | | | n_neighbors | The number of neighbors. | integer | | output_names | The names of the output variables. if ``none``, consider all the output variables of the | null | | | learning dataset. | | | transformer | The strategies to transform the variables. the values are instances of | object | | | :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 :attr:`.identity`, do not transform the variables. | | +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ INFO - 10:53:15: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ INFO - 10:53:15: | Name | Description | Type | INFO - 10:53:15: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ INFO - 10:53:15: | input_names | The names of the input variables. if ``none``, consider all the input variables of the | null | INFO - 10:53:15: | | learning dataset. | | INFO - 10:53:15: | n_neighbors | The number of neighbors. | integer | INFO - 10:53:15: | output_names | The names of the output variables. if ``none``, consider all the output variables of the | null | INFO - 10:53:15: | | learning dataset. | | INFO - 10:53:15: | transformer | The strategies to transform the variables. the values are instances of | object | INFO - 10:53:15: | | :class:`.transformer` while the keys are the names of either the variables or the groups | | INFO - 10:53:15: | | of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression algorithms. | | INFO - 10:53:15: | | if a group is specified, the :class:`.transformer` will be applied to all the variables of | | INFO - 10:53:15: | | this group. if :attr:`.identity`, do not transform the variables. | | INFO - 10:53:15: +---------------------------+--------------------------------------------------------------------------------------------+---------------------------+ {'$schema': 'http://json-schema.org/schema#', 'type': 'object', 'properties': {'transformer': {'description': '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 :attr:`.IDENTITY`, do not transform the variables.', 'type': 'object'}, 'input_names': {'description': 'The names of the input variables. If ``None``, consider all the input variables of the learning dataset.', 'type': 'null'}, 'output_names': {'description': 'The names of the output variables. If ``None``, consider all the output variables of the learning dataset.', 'type': 'null'}, 'n_neighbors': {'description': 'The number of neighbors.', 'type': 'integer'}}} .. GENERATED FROM PYTHON SOURCE LINES 51-53 Create classification model --------------------------- .. GENERATED FROM PYTHON SOURCE LINES 53-58 .. code-block:: Python iris = create_benchmark_dataset("IrisDataset", as_io=True) model = create_classification_model("KNNClassifier", data=iris) model.learn() model .. raw:: html
KNNClassifier(n_neighbors=5)
  • based on the scikit-learn library
  • built from 150 learning samples


.. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.041 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 ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_classification_api.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_