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.
For this engine, there is a single mode: classification
This engine has no tuning parameters.
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())
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.
The underlying model implementation does not allow for case weights.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
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