Feature extraction

At some point, after we have removed redundant features and dropped irrelevant ones, we, often, still find that we have too many features. No matter what learning method we use, they all perform badly and given the huge feature space we understand that they actually cannot do better. We realize that we have to cut living flesh; we have to get rid of features, for which all common sense tells us that they are valuable. Another situation when we need to reduce the dimensions and feature selection does not help much is when we want to visualize data. Then, we need to have at most three dimensions at the end to provide any meaningful graphs.

Enter feature extraction methods. They restructure the feature space to make it more accessible ...

Get Building Machine Learning Systems with Python - Second Edition now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.