# modelLookup

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

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

- Keywords
- utilities

##### Usage

`modelLookup(model = NULL)`

##### 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

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
model a character string for the model code parameter the tuning parameter name label a tuning parameter label (used in plots) seq a logical; can sub-models be used to decrease training time (see the Details section) forReg a logical; can the model be used for regression? forClass a logical; can the model be used for classification? probModel a logical; does the model produce class probabilities?

##### See Also

##### Examples

```
modelLookup()
modelLookup("gbm")
```

*Documentation reproduced from package caret, version 5.07-001, 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"]]) ```