This function predicts classes of new objects with given rFerns
object.
# S3 method for rFerns
predict(object, x, scores = FALSE, ...)
Object of a class rFerns
; a model that will be used for prediction.
Data frame containing attributes; must have corresponding names to training set (although order is not important) and do not introduce new factor levels. If this argument is not given, OOB predictions on the training set will be returned.
If TRUE
, the result will contain score matrix instead of simple predictions.
Additional parameters.
Predictions.
If scores
is TRUE
, a factor vector (for many-class classification) or a logical data.frame (for multi-class classification) with predictions, else a data.frame with class' scores.
# NOT RUN { set.seed(77) #Fetch Iris data data(iris) #Split into tRain and tEst set iris[c(TRUE,FALSE),]->irisR iris[c(FALSE,TRUE),]->irisE #Build model rFerns(Species~.,data=irisR)->model print(model) #Test predict(model,irisE)->p print(table( Predictions=p, True=irisE[["Species"]])) err<-mean(p!=irisE[["Species"]]) print(paste("Test error",err,sep=" ")) #Show first OOB scores head(predict(model,scores=TRUE)) # }