Prediction with random ferns model
This function predicts classes of new objects with given rFerns object.
## S3 method for class 'rFerns' predict(object, x, scores = FALSE, ...)
object |
Object of a class |
x |
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. |
scores |
If |
... |
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.
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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.