Summary

In this chapter, we continued our journey of supervised learning with SVM. You learned about 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 you learned in the form of real-world use cases, including face recognition and fetal state classification.

You 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 to independent, features, Naïve Bayes will usually perform well. SVM is versatile and adaptive to the linear separability of data. In general, high accuracy can be achieved by SVM with the right kernel and parameters. However...