March 2019
Intermediate to advanced
642 pages
22h 54m
English
Content preview from Python Machine Learning Cookbook - Second Edition
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The advantage of selecting the k best features is that we will be able to work with low-dimensional data. This is helpful in reducing the computational complexity. The way in which we select the k best features is based on univariate feature selection. This performs univariate statistical tests and then extracts the top performing features from the feature vector. Univariate statistical tests refer to analysis techniques where a single variable is involved.
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