Overview

Data science includes a set of statistical techniques that allow extracting the knowledge immersed in the data automatically. One of the fundamental tools in data science are classification techniques. This book develops parametric classification supervised techniques such as decision trees and discriminant analysis models. It also develops non-supervised analysis techniques such as cluster analysis.Cluster analysis, also called segmentation analysis or taxonomy analysis, creates groups, or clusters, of data. Clusters are formed in such a way that objects in the same cluster are very similar and objects in different clusters are very distinct. Measures of similarity depend on the application.Decision trees, or classification trees and regression trees, predict responses to data. To predict a response, follow the decisions in the tree from the root (beginning) node downto a leaf node. The leaf node contains the response. Classification trees give responses that are nominal, such as 'true' or 'false'. Regression trees give numeric responses. Statistics and Machine Learning Toolbox trees are binary. Each step in a prediction involves checking the value of one predictor (variable).Discriminant analysis is a classification method. It assumes that differen classes generate data based on different Gaussian distributions. To train (create) a classifier, the fitting function estimates the parameters of a Gaussian distribution for each class (see Creating Discriminant Analysis Model ).-To predict the classes of new data, the trained classifier find the class with the smallest misclassification cost (see Prediction Using Discriminant Analysis Models ).Linear discriminant analysis is also known as the Fisher discriminant, named for its inventor.The naive Bayes classifier is designed for use when predictors are independent of one another within each class, but it appears to work well in practice even when that independence assumption is not valid.

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