machine learning with python by Andreas C. Mueller & Sarah Guido

Author:Andreas C. Mueller & Sarah Guido
Language: eng
Format: epub
Publisher: O'Reilly Media, Inc.
Published: 2016-06-09T04:00:00+00:00

array([-1, 0])

Using a much larger eps=15 we get only a single clusters and noise points. We can use this result and find out what the “noise” looks like compared to the rest of the data. To understand better what’s happening, let’s look at how many points are noise, and how many points are inside the cluster:

# count number of points in all clusters and noise. # bincount doesn't allow negative numbers, so we need to add 1. # the first number in the result corresponds to noise points np.bincount(labels + 1)

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