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SUPPORT VECTOR MACHINES
In this chapter, we discuss a relatively new regression analysis technique called support vector machines, or SVM for short. SVM is considered one of the best classifiers in supervised learning for analyzing complex data and downplaying the influence of outliers.
Developed within the computer science community in the 1990s, SVM was initially designed for predicting numeric and categorical outcomes as a double-barrel prediction technique. Today, SVM is mostly used as a classification technique for predicting categorical outcomes—similar to logistic regression.
Figure 27: Logistic regression versus SVM
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