man/rmd/discrim_regularized_klaR.md

In topepo/parsnip: A Common API to Modeling and Analysis Functions

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.

library(discrim)

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

## 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 \code{\link[=fit.model_spec]{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.

References

• 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.

topepo/parsnip documentation built on April 16, 2024, 3:23 a.m.