details_discrim_linear_mda: Linear discriminant analysis via flexible discriminant...

In parsnip: A Common API to Modeling and Analysis Functions

Linear discriminant analysis via flexible discriminant analysis

Description

mda::fda() (in conjunction with mda::gen.ridge() can fit a linear discriminant analysis model that penalizes the predictor coefficients with a quadratic penalty (i.e., a ridge or weight decay approach).

Details

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 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 June 24, 2024, 5:14 p.m.