July 2018
Beginner to intermediate
376 pages
9h 1m
English
The Adaptive boosting algorithm section in the previous chapter contained m models, classifiers
, n observations and weights, and a voting power that is determined sequentially. The adaptation of the adaptive boosting method was illustrated using a toy example, and then applied using specialized functions. When compared with the bagging and random forest methods, we found that boosting provides the highest accuracy, which you may remember from the results in the aforementioned section in the previous chapter. However, the implementation of the algorithm does not tell us why it was expected to perform better.
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