predict.rFerns: Prediction with random ferns model

In rFerns: Random Ferns Classifier

Description

This function predicts classes of new objects with given rFerns object.

Usage

## S3 method for class 'rFerns'
predict(object, x, scores = FALSE, ...)

Arguments

object	Object of a class rFerns; a model that will be used for prediction.
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 TRUE, the result will contain score matrix instead of simple predictions.
...	Additional parameters.

Value

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.

Examples

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))

Example output

 Forest of 1000 ferns of a depth 5.

 OOB error 5.33%; OOB confusion matrix:
            True
Predicted    setosa versicolor virginica
  setosa         25          0         0
  versicolor      0         23         2
  virginica       0          2        23
            True
Predictions  setosa versicolor virginica
  setosa         25          0         0
  versicolor      0         23         2
  virginica       0          2        23
[1] "Test error 0.0533333333333333"
    setosa versicolor virginica
1 365.5591  -611.7447 -689.0393
2 355.2148  -532.3447 -656.8028
3 356.9968  -611.9595 -655.4825
4 335.4048  -542.7392 -608.9734
5 207.1545  -309.8834 -452.9373
6 326.7404  -532.5410 -609.7109

rFerns documentation built on Sept. 22, 2021, 5:10 p.m.