Partial least squares (PLS)Source:
pls() defines a partial least squares model that uses latent variables to
model the data. It is similar to a supervised version of principal component.
This function can fit classification and regression 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.
More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification".
The maximum proportion of original predictors that can have non-zero coefficients for each PLS component (via regularization). This value is used for all PLS components for X.
The number of PLS components to retain.
A single character string specifying what computational engine to use for fitting.
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
The model is not trained or fit until the
fit() function is used
with the data.