Prediction for Isopro for Identifying Anomalous Data

Use isolation forests to identify rare/anomalous values using test data.

predict.isopro(object, newdata, quantiles = TRUE, ...)

Arguments

object

isopro object obtained from previous call.

newdata

Test data. If not provided the training data is used.

quantiles

Return quantiles (default) or case depth values?

...

Additional options.

Details

Uses previously constructed isopro object to identify anomalous observations in test data. Values returned are quantile values indicating depth of test data relative to original training data. Small values indicate outliers. To return depth values, use quantiles="FALSE".

Author

Min Lu and Hemant Ishwaran

References

Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. (2008). Isolation forest. 2008 Eighth IEEE International Conference on Data Mining. IEEE.

Ishwaran H. (2025). Multivariate Statistics: Classical Foundations and Modern Machine Learning, CRC (Chapman and Hall), in press.

Examples

# \donttest{
## ------------------------------------------------------------
##
## boston housing
## unsupervised isopro analysis
##
## ------------------------------------------------------------

## training
data(BostonHousing, package = "mlbench")
o <- isopro(data=BostonHousing)

## make fake data
fake <- do.call(rbind, lapply(1:nrow(BostonHousing), function(i) {
  fakei <- BostonHousing[i,]
  fakei$lstat <- quantile(BostonHousing$lstat, .99)
  fakei$nox <- quantile(BostonHousing$nox, .99)
  fakei
}))

## compare depth values for fake data to training data
depth.fake <- predict(o, fake)
depth.train <- predict(o)
depth.data <- rbind(data.frame(whichdata="fake", depth=depth.fake),
                    data.frame(whichdata="train", depth=depth.train))
boxplot(depth~whichdata, depth.data, xlab="data", ylab="depth quantiles")


## ------------------------------------------------------------
##
## boston housing
## isopro supervised analysis with different split rules
##
## ------------------------------------------------------------

data(BostonHousing, package="mlbench")

## supervised isopro analysis using different splitrules
o <- isopro(formula=medv~.,data=BostonHousing)
o.hvwt <- isopro(formula=medv~.,data=BostonHousing,splitrule="mse.hvwt")
o.unwt <- isopro(formula=medv~.,data=BostonHousing,splitrule="mse.unwt")
     
## make fake data
fake <- do.call(rbind, lapply(1:nrow(BostonHousing), function(i) {
  fakei <- BostonHousing[i,]
  fakei$lstat <- quantile(BostonHousing$lstat, .99)
  fakei$nox <- quantile(BostonHousing$nox, .99)
  fakei
}))

## compare depth values for fake data to training data
depth.train <- predict(o)
depth.hvwt.train <- predict(o.hvwt)
depth.unwt.train <- predict(o.unwt)
depth.fake <- predict(o, fake)
depth.hvwt.fake <- predict(o.hvwt, fake)
depth.unwt.fake <- predict(o.unwt, fake)
depth.data <- rbind(data.frame(whichdata="fake", depth=depth.fake),
                    data.frame(whichdata="fake.hvwt", depth=depth.hvwt.fake),
                    data.frame(whichdata="fake.unwt", depth=depth.unwt.fake),
                    data.frame(whichdata="train", depth=depth.train),
                    data.frame(whichdata="train.hvwt", depth=depth.hvwt.train),
                    data.frame(whichdata="train.unwt", depth=depth.unwt.train))
boxplot(depth~whichdata, depth.data, xlab="data", ylab="depth quantiles")

# }