predict.CoreModel | R Documentation |

Using a previously built model and new data, predicts the class value and probabilities for classification problem and function value for regression problem.

## S3 method for class 'CoreModel' predict(object, newdata, ..., costMatrix=NULL, type=c("both","class","probability"))

`object` |
The model structure as returned by |

`newdata` |
Data frame with fresh data. |

`costMatrix` |
Optional cost matrix can provide nonuniform costs for classification problems. |

`type` |
Controls what will be return value in case of classification. |

`... ` |
Other model dependent options for prediction. See |

The function uses the `object`

structure as returned by `CoreModel`

and
applies it on the data frame `newdata`

. The `newdata`

must be transformable
using the formula specified for building the model (with dependent variable removed). If the dependent
variable is present in `newdata`

, it is ignored.

Optional cost matrix can provide nonuniform costs for classification problems. For regression
problem this parameter is ignored. The costs can be different from the ones used for building the model
in `CoreModel`

.

For regression model a vector of predicted values for given input instances. For classification
problem the parameter `type`

controls what is returned. With default value `"both"`

function returns a list with two components `class`

and `probabilities`

containing predicted class values and probabilities for all class values, respectively.
With `type`

set to `"class"`

or `"probability"`

the function returns only the selected component
as vector or matrix.

Marko Robnik-Sikonja, Petr Savicky

`CORElearn`

,
`CoreModel`

,
`modelEval`

,
`helpCore`

,
`paramCoreIO`

.

# use iris data set # build random forests model with certain parameters modelRF <- CoreModel(Species ~ ., iris, model="rf", selectionEstimator="MDL",minNodeWeightRF=5,rfNoTrees=100) print(modelRF) # prediction with node distribution pred <- predict(modelRF, iris, rfPredictClass=FALSE, type="both") # print(pred) destroyModels(modelRF) # clean up

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