rda
Regularized discriminant analysis. RDA is a compromise between LDA and QDA, which allows one to shrink the separate covariances of QDA toward a common variance as in LDA. This method is very similar in flavor to ridge regression. The regularized covariance matrices of each class is Σk(α) = α Σk + (1 - α) Σ. The quadratic discriminant function is defined using the shrunken covariance matrices Σk(α). The parameter α in [0, 1] controls the complexity of the model. When α is one, RDA becomes QDA. While α is zero, RDA is equivalent to LDA. Therefore, the regularization factor α allows a continuum of models between LDA and QDA.
Return
Regularized discriminant analysis model.
Parameters
training samples.
training labels in [0, k), where k is the number of classes.
regularization factor in [0, 1] allows a continuum of models between LDA and QDA.
the priori probability of each class.
tolerance to decide if a covariance matrix is singular; it will reject variables whose variance is less than tol2.