August 2018
Intermediate to advanced
522 pages
12h 45m
English
Content preview from Machine Learning Algorithms - Second Edition
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Agglomerative Clustering
Let's consider the following dataset:
We define affinity, a metric function of two arguments with the same dimensionality, m. The most common metrics (also supported by scikit-learn) are the following:
- Euclidean or L2 (Minkowski distance with p=2):
- Manhattan (also known as city block) or L1 (Minkowski distance with p=1):
- Cosine distance:
The Euclidean distance is normally a good choice, but sometimes it's useful ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.