Flexible discriminant analysis via earth
Source:R/discrim_flexible_earth.R
details_discrim_flexible_earth.Rdmda::fda() (in conjunction with earth::earth() can fit a nonlinear
discriminant analysis model that uses nonlinear features created using
multivariate adaptive regression splines (MARS). This function can fit
classification models.
Details
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 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.