October 2016
Intermediate to advanced
654 pages
22h 41m
English
Content preview from Data Mining, 4th Edition
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Chapter 5
Credibility
Evaluating what’s been learned
Abstract
The success of machine learning in practical applications hinges on proper evaluation. This section discusses how the quality of predictions can be measured reliably. We consider the basic train-test setup for estimating predictive accuracy, before moving on to more sophisticated variants known as “cross-validation” and the “bootstrap” method. We also discuss the importance of proper parameter tuning when applying and evaluating machine learning, and explain how to use statistical significance tests when comparing the performance of two learning algorithms in a particular application domain. As well as basic classification accuracy, we consider other measures for evaluating the quality ...
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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.