.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/mlearning/classification_model/plot_knn.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_mlearning_classification_model_plot_knn.py: K nearest neighbors classification ================================== We want to classify the Iris dataset using a KNN classifier. .. GENERATED FROM PYTHON SOURCE LINES 29-39 .. code-block:: default from __future__ import division, unicode_literals from numpy import array from gemseo.api import configure_logger, load_dataset from gemseo.mlearning.api import create_classification_model configure_logger() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 40-42 Load Iris dataset ----------------- .. GENERATED FROM PYTHON SOURCE LINES 42-44 .. code-block:: default iris = load_dataset("IrisDataset", as_io=True) .. GENERATED FROM PYTHON SOURCE LINES 45-49 Create the classification model ------------------------------- Then, we build the linear regression model from the discipline cache and displays this model. .. GENERATED FROM PYTHON SOURCE LINES 49-53 .. code-block:: default model = create_classification_model("KNNClassifier", data=iris) model.learn() print(model) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none KNNClassifier(n_neighbors=5) based on the scikit-learn library built from 150 learning samples .. GENERATED FROM PYTHON SOURCE LINES 54-57 Predict output -------------- Once it is built, we can use it for prediction. .. GENERATED FROM PYTHON SOURCE LINES 57-65 .. 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 Out: .. code-block:: none {'specy': array([0])} .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.006 seconds) .. _sphx_glr_download_examples_mlearning_classification_model_plot_knn.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_knn.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_knn.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_