Summary

In this chapter, we explained the issue of encountering negative raw probabilities that are generated by building a binary classification probability model based strictly on linear regression, where probabilities in a range of [0, 1] are expected. We provided an overview of the log-odds ratio and probit and logit modeling using the cumulative distribution function of both the standard normal distribution and logistic distribution, respectively. We also demonstrated methods for applying logistic regression to solve binary and multinomial classification problems. Lastly, we covered count-based regression using the log-linear Poisson and negative binomial models, which can also be logically extended to rate data without modification. We provided examples of their implementations.

In the following chapter, we will introduce conditional probability using Bayes’ theorem in addition to dimension reduction and classification modeling using linear discriminant analysis and...