What theorem is fundamental to the concept of linear discriminant analysis? Central limit theorem No free lunch theorem Bayes' theorem Occam's razor

Answer

The theorem that is fundamental to the concept of linear discriminant analysis (LDA) is Bayes' theorem.

LDA is a classification technique that is used to find a linear combination of features that best separates two or more classes of objects or events. It relies on Bayes' theorem to compute the conditional probabilities of the classes given the features. By using these probabilities, LDA assumes that the classes differ in their means and covariances, allowing it to find a decision boundary that maximizes the separation between the classes based on the distributions of the data.

In summary, while all the mentioned theorems have their own significance in different contexts, Bayes' theorem is the one that closely ties in with the principles underlying linear discriminant analysis.