May 2018
Intermediate to advanced
576 pages
14h 42m
English
Content preview from Mastering Machine Learning Algorithms
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Semi-supervised assumptions
As explained in the previous section, semi-supervised learning is not guaranteed to improve a supervised model. A wrong choice could lead to a dramatic worsening in performance; however, it's possible to state some fundamental assumptions which are required for semi-supervised learning to work properly. They are not always mathematically proven theorems, but rather empirical observations that justify the choice of an approach otherwise completely arbitrary.
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