ABSTRACT

This chapter provides a detailed overview of several statistical supervised data mining techniques. It helps in learning how the Bayes classifier builds supervised models for both categorical and real-valued data. The naive Bayes classifier offers a simple yet powerful supervised classification technique. The model assumes all input attributes to be of equal importance and independent of one another. Even though these assumptions are likely to be false, the Bayes classifier still works quite well in practice. The chapter introduces support vector machines for supervised learning and covers simple and multiple linear regression. It discusses logistic regression and how it is applied to build supervised learner models for data sets with a binary outcome. A favorite statistical technique for estimation and prediction problems is linear regression. Linear regression attempts to model the variation in a numeric dependent variable as a linear combination of one or more numeric independent variables.