This chapter covers
- Understanding linear and quadratic discriminant analysis
- Building discriminant analysis classifiers to predict wines
Discriminant analysis is an umbrella term for multiple algorithms that solve classification problems (where we wish to predict a categorical variable) in a similar way. While there are various discriminant analysis algorithms that learn slightly differently, they all find a new representation of the original data that maximizes the separation between the classes.
Recall from chapter 1 that predictor variables are the variables we hope contain the information needed to make predictions on new data. Discriminant function analysis algorithms find a new representation of the predictor variables (which must be continuous) by combining them together into new variables that best discriminate the classes. This combination of predictor variables often has the handy benefit of reducing the number of predictors to a much smaller number. Because of this, despite discriminant analysis algorithms being classification algorithms, they are similar to some of the dimension-reduction algorithms we’ll encounter in part 4 of the book.