In this chapter, we learned how to perform an accurate classification in a MATLAB environment. First, we explored decision trees methods; we learned concepts like nodes, branches, and leaf nodes. We saw how to classify objects into a finite number of classes by repeatedly dividing the records into homogeneous subsets with respect to the target attribute. Then, we looked at how to predict a response with decision trees.
Next, we discovered the probabilistic classification algorithm that determines the probability that an element belongs to a particular class. We learned the basic concepts of probability theory: classical probability definition, dependent and independent events, joint probability and conditional probability, which is the basis of these methods. Then, we understood how to classify with the Naive Bayes algorithm.
We explored discriminant analysis methodologies; several examples were analyzed to compare the relative results. We learned how to create models to minimize the...