Supervised Classification Part 2
Continuing in the present chapter on the subject of supervised classification, we will begin with a discussion of postclassification processing methods to improve classification results on the basis of contextual information. Then we turn our attention to statistical procedures for evaluating classification accuracy and for making quantitative comparisons between different classifiers. In this context, the computationally expensive n-fold cross-validation procedure will provide a good excuse to illustrate again how to take advantage of the parallel computing capability of IPython engines. As an example of so-called ensembles or committees of classifiers, we then examine the adaptive boosting technique, applying ...
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