man/rmd/discrim_flexible_earth.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 3 tuning parameter:

• num_terms: # Model Terms (type: integer, default: (see below))

• prod_degree: Degree of Interaction (type: integer, default: 1L)

• prune_method: Pruning Method (type: character, default: 'backward')

The default value of num_terms depends on the number of columns (p): min(200, max(20, 2 * p)) + 1. Note that num_terms = 1 is an intercept-only model.

Translation from parsnip to the original package

The discrim extension package is required to fit this model.

library(discrim)

discrim_flexible(
  num_terms = integer(0),
  prod_degree = integer(0),
  prune_method = character(0)
) |> 
  translate()

## Flexible Discriminant Model Specification (classification)
## 
## Main Arguments:
##   num_terms = integer(0)
##   prod_degree = integer(0)
##   prune_method = character(0)
## 
## Computational engine: earth 
## 
## Model fit template:
## mda::fda(formula = missing_arg(), data = missing_arg(), weights = missing_arg(), 
##     nprune = integer(0), degree = integer(0), pmethod = character(0), 
##     method = earth::earth)

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.

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.

Prediction types

parsnip:::get_from_env("discrim_flexible_predict") |>
  dplyr::select(mode, type)

## # A tibble: 3 x 2
##   mode           type 
##   <chr>          <chr>
## 1 classification class
## 2 classification prob 
## 3 classification raw

References

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

• Friedman (1991). Multivariate Adaptive Regression Splines. The Annals of Statistics, 19(1), 1-67.

parsnip documentation built on Jan. 11, 2026, 9:06 a.m.