scikit-learn Cookbook by Hauck Trent;

Author:Hauck, Trent;
Language: eng
Format: epub
Publisher: Packt Publishing

First, we'll look at silhouette distance. Silhouette distance is the ratio of the difference between in-cluster dissimilarity, the closest out-of-cluster dissimilarity, and the maximum of these two values. It can be thought of as a measure of how separate the clusters are.

Let's look at the distribution of distances from the points to the cluster centers; it's useful to understand silhouette distances:

>>> from sklearn import metrics >>> silhouette_samples = metrics.silhouette_samples(blobs, kmean.labels_) >>> np.column_stack((classes[:5], silhouette_samples[:5])) array([[ 1., 0.87617292], [ 1., 0.89082363], [ 1., 0.88544994], [ 1., 0.91478369], [ 1., 0.91308287]]) >>> f, ax = plt.subplots(figsize=(10, 5)) >>> ax.set_title("Hist of Silhouette Samples") >>> ax.hist(silhouette_samples)

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