In this chapter, we continued our journey of classifying news data with the SVM classifier, where we acquired the mechanics of an SVM, kernel techniques and implementations of SVM, and other important concepts of machine learning classification, including multiclass classification strategies and grid search, as well as useful tips for using an SVM (for example, choosing between kernels and tuning parameters). Then, we finally put into practice what we had learned in the form of two use cases: news topic classification and fetal state classification.

We have learned and adopted two classification algorithms so far, Naïve Bayes and SVM. Naïve Bayes is a simple algorithm (as its name implies). For a dataset with independent, or close ...

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