# modelLookup

0th

Percentile

##### Descriptions Of Models Available in train()

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

Keywords
utilities
##### Usage
modelLookup(model = NULL)
##### Arguments
model
a character string associated with the method argument of train. If no value is passed, all models are returned
##### 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.

##### 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?

train

• modelLookup
##### Examples
modelLookup()

modelLookup("gbm")
Documentation reproduced from package caret, version 4.65, License: GPL-2

### Community examples

cbuctok@gmail.com at Jan 9, 2018 caret v6.0-78

Actually example doesn't work for me this one works: r checkInstall(getModelInfo("OneR")[["OneR"]][["library"]])