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klaR::rda() fits a a model that estimates a multivariate distribution for the predictors separately for the data in each class. The structure of the model can be LDA, QDA, or some amalgam of the two. Bayes' theorem is used to compute the probability of each class, given the predictor values.


For this engine, there is a single mode: classification

Tuning Parameters

This model has 2 tuning parameter:

  • frac_common_cov: Fraction of the Common Covariance Matrix (type: double, default: (see below))

  • frac_identity: Fraction of the Identity Matrix (type: double, default: (see below))

Some special cases for the RDA model:

  • frac_identity = 0 and frac_common_cov = 1 is a linear discriminant analysis (LDA) model.

  • frac_identity = 0 and frac_common_cov = 0 is a quadratic discriminant analysis (QDA) model.

Translation from parsnip to the original package

The discrim extension package is required to fit this model.


discrim_regularized(frac_identity = numeric(0), frac_common_cov = numeric(0)) %>% 
  set_engine("klaR") %>% 

## Regularized Discriminant Model Specification (classification)
## Main Arguments:
##   frac_common_cov = numeric(0)
##   frac_identity = numeric(0)
## Computational engine: klaR 
## Model fit template:
## klaR::rda(formula = missing_arg(), data = missing_arg(), lambda = numeric(0), 
##     gamma = numeric(0))

Preprocessing requirements

Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via fit(), parsnip will convert factor columns to indicators.

Variance calculations are used in these computations within each outcome class. For this reason, zero-variance predictors (i.e., with a single unique value) within each class should be eliminated before fitting the model.

Case weights

The underlying model implementation does not allow for case weights.


  • Friedman, J (1989). Regularized Discriminant Analysis. Journal of the American Statistical Association, 84, 165-175.

  • Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.