discrim_linear() defines a model that estimates a multivariate
distribution for the predictors separately for the data in each class
(usually Gaussian with a common covariance matrix). Bayes' theorem is used
to compute the probability of each class, given the predictor values. This
function can fit classification models.
There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.
¹ The default engine. ² Requires a parsnip extension package.More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
Usage
discrim_linear(
mode = "classification",
penalty = NULL,
regularization_method = NULL,
engine = "MASS"
)Arguments
- mode
A single character string for the type of model. The only possible value for this model is "classification".
- penalty
An non-negative number representing the amount of regularization used by some of the engines.
- regularization_method
A character string for the type of regularized estimation. Possible values are: "
diagonal", "min_distance", "shrink_cov", and "shrink_mean" (sparsediscrimengine only).- engine
A single character string specifying what computational engine to use for fitting.
Details
This function only defines what type of model is being fit. Once an engine
is specified, the method to fit the model is also defined. See
set_engine() for more on setting the engine, including how to set engine
arguments.
The model is not trained or fit until the fit() function is used
with the data.
Each of the arguments in this function other than mode and engine are
captured as quosures. To pass values
programmatically, use the injection operator like so:
value <- 1
discrim_linear(argument = !!value)