Discriminant analysis overview

Discriminant Analysis (DA), also known as Fisher Discriminant Analysis (FDA), is another popular classification technique. It can be an effective alternative to logistic regression when the classes are well-separated. If you have a classification problem where the outcome classes are well-separated, logistic regression can have unstable estimates, which is to say that the confidence intervals are wide and the estimates themselves likely vary from one sample to another (James, 2013). DA does not suffer from this problem and, as a result, may outperform and be more generalized than logistic regression. Conversely, if there are complex relationships between the features and outcome variables, it may perform poorly on a classification task. For our breast cancer example, logistic regression performed well on the testing and training sets, and the classes were not well-separated. For the purpose of comparison with logistic regression, we will explore DA, both Linear...