.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. 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. .. rst-class:: sphx-glr-horizontal * .. image:: /auto_examples/neighbors/images/sphx_glr_plot_nearest_centroid_001.png :class: sphx-glr-multi-img * .. image:: /auto_examples/neighbors/images/sphx_glr_plot_nearest_centroid_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none None 0.8133333333333334 0.2 0.82 | .. code-block:: python print(__doc__) import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap from sklearn import datasets from sklearn.neighbors import NearestCentroid n_neighbors = 15 # 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 h = .02 # step size in the mesh # Create color maps cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF']) cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF']) for shrinkage in [None, .2]: # we create an instance of Neighbours 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)) # Plot the decision boundary. For that, we will assign a color to each # point in the mesh [x_min, x_max]x[y_min, y_max]. x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) plt.figure() plt.pcolormesh(xx, yy, Z, cmap=cmap_light) # 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() **Total running time of the script:** ( 0 minutes 0.051 seconds) .. _sphx_glr_download_auto_examples_neighbors_plot_nearest_centroid.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_nearest_centroid.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_nearest_centroid.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_