Linear discriminant analysis via MASS

MASS::lda() fits a model that estimates a multivariate distribution for the predictors separately for the data in each class (Gaussian with a common covariance matrix). Bayes' theorem is used to compute the probability of each class, given the predictor values.

Details

For this engine, there is a single mode: classification

Tuning Parameters

This engine has no tuning parameters.

Translation from parsnip to the original package

The discrim extension package is required to fit this model.

library(discrim)

discrim_linear() %>% 
  set_engine("MASS") %>% 
  translate()

## Linear Discriminant Model Specification (classification)
## 
## Computational engine: MASS 
## 
## Model fit template:
## MASS::lda(formula = missing_arg(), data = missing_arg())

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

The underlying model implementation does not allow for case weights.

References

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.