.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/neighbors/plot_nearest_centroid.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. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_neighbors_plot_nearest_centroid.py: =============================== Nearest Centroid Classification =============================== Sample usage of Nearest Centroid classification. It will plot the decision boundaries for each class. .. GENERATED FROM PYTHON SOURCE LINES 10-51 .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_nearest_centroid_001.png :alt: 3-Class classification (shrink_threshold=None) :srcset: /auto_examples/neighbors/images/sphx_glr_plot_nearest_centroid_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_nearest_centroid_002.png :alt: 3-Class classification (shrink_threshold=0.2) :srcset: /auto_examples/neighbors/images/sphx_glr_plot_nearest_centroid_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none None 0.8133333333333334 0.2 0.82 | .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import matplotlib.pyplot as plt import numpy as np from matplotlib.colors import ListedColormap from sklearn import datasets from sklearn.inspection import DecisionBoundaryDisplay from sklearn.neighbors import NearestCentroid # import some data to play with iris = datasets.load_iris() # we only take the first two features. We could avoid this ugly # slicing by using a two-dim dataset X = iris.data[:, :2] y = iris.target # Create color maps cmap_light = ListedColormap(["orange", "cyan", "cornflowerblue"]) cmap_bold = ListedColormap(["darkorange", "c", "darkblue"]) for shrinkage in [None, 0.2]: # we create an instance of Nearest Centroid Classifier and fit the data. clf = NearestCentroid(shrink_threshold=shrinkage) clf.fit(X, y) y_pred = clf.predict(X) print(shrinkage, np.mean(y == y_pred)) _, ax = plt.subplots() DecisionBoundaryDisplay.from_estimator( clf, X, cmap=cmap_light, ax=ax, response_method="predict" ) # Plot also the training points plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor="k", s=20) plt.title("3-Class classification (shrink_threshold=%r)" % shrinkage) plt.axis("tight") plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.168 seconds) .. _sphx_glr_download_auto_examples_neighbors_plot_nearest_centroid.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/neighbors/plot_nearest_centroid.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/neighbors/plot_nearest_centroid.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_nearest_centroid.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_nearest_centroid.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_nearest_centroid.zip ` .. include:: plot_nearest_centroid.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_