man/rmd/discrim_linear_mda.md

In parsnip: A Common API to Modeling and Analysis Functions

For this engine, there is a single mode: classification

Tuning Parameters

This model has 1 tuning parameter:

• penalty: Amount of Regularization (type: double, default: 1.0)

Translation from parsnip to the original package

The discrim extension package is required to fit this model.

library(discrim)

discrim_linear(penalty = numeric(0)) %>% 
  set_engine("mda") %>% 
  translate()

## Linear Discriminant Model Specification (classification)
## 
## Main Arguments:
##   penalty = numeric(0)
## 
## Computational engine: mda 
## 
## Model fit template:
## mda::fda(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
##     lambda = numeric(0), method = mda::gen.ridge, keep.fitted = FALSE)

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 so zero-variance predictors (i.e., with a single unique value) should be eliminated before fitting the model.

Case weights

This model can utilize case weights during model fitting. To use them, see the documentation in [case_weights] and the examples on tidymodels.org.

The fit() and fit_xy() arguments have arguments called case_weights that expect vectors of case weights.

References

• Hastie, Tibshirani & Buja (1994) Flexible Discriminant Analysis by Optimal Scoring, Journal of the American Statistical Association, 89:428, 1255-1270

parsnip documentation built on Aug. 18, 2023, 1:07 a.m.