Mastering Python for Finance - Second Edition

Linear discriminant analysis

Linear discriminant analysis (LDA) is a classic classifier that uses a linear decision surface, where the mean and variance for every class of the data is estimated. It assumes that the data is Gaussian, and that each attribute has the same variance, and values of each variable are around the mean. LDA computes discriminant scores by using Bayes' theorem for each observation to determine to which class it belongs.

The LinearDiscriminantAnalysis class of the sklearn.discriminant_analysis module implements the LDA classifier.