predict.CoreModel: Prediction using constructed model

View source: R/Rinterface.R

predict.CoreModelR Documentation

Prediction using constructed model


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, 



The model structure as returned by CoreModel.


Data frame with fresh data.


Optional cost matrix can provide nonuniform costs for classification problems.


Controls what will be return value in case of classification.


Other model dependent options for prediction. See helpCore.


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

See Also

CORElearn, CoreModel, modelEval, helpCore, paramCoreIO.


# use iris data set

# build random forests model with certain parameters
modelRF <- CoreModel(Species ~ ., iris, model="rf", 

# prediction with node distribution
pred <- predict(modelRF, iris, rfPredictClass=FALSE, type="both")
# print(pred)

destroyModels(modelRF) # clean up

CORElearn documentation built on Nov. 18, 2022, 5:08 p.m.