modelLookup: Descriptions Of Models Available in train()

Description

This function enumerates the parameters and characteristics for models used by train

Usage

modelLookup(model = NULL)

Arguments

Value

a data frame with columns
modela character string for the model code
parameterthe tuning parameter name
labela tuning parameter label (used in plots)
seqa logical; can sub-models be used to decrease training time (see the Details section)
forRega logical; can the model be used for regression?
forClassa logical; can the model be used for classification?
probModela logical; does the model produce class probabilities?

Details

One characteristic listed in the output is whether or not sub-models can be used for prediction. For example, if a PLS model is fit with X components, PLS models with train exploits this characteristic whenever possible.

These types of tuning parameters are defined as "sequential" parameters since one value can be used to derive a sequences of predictions. Examples of model codes that include sequential tuning parameters are; blackboost, ctree, earth, enet, foba, gamboost, gbm, glmboost, glmnet, lars, lars2, lasso, logitBoost, pam, partDSA, pcr, pls, relaxo, rpart, scrda and superpc.

Examples

Run this code

modelLookup()

modelLookup("gbm")