predict.isopro: Prediction for Isopro for Identifying Anomalous Data

In kogalur/varPro: Model-Independent Variable Selection via the Rule-Based Variable Priority (VarPro)

Prediction for Isopro for Identifying Anomalous Data

Description

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

Usage

## S3 method for class 'isopro'
predict(object, newdata, quantiles = TRUE, ...)

Arguments

object	isopro object returned from a previous call.
newdata	Optional test data. If not provided, the training data is used.
quantiles	Logical. If TRUE (default), returns quantile values; if FALSE, returns case depth values.
...	Additional arguments passed to internal methods.

Details

Uses a previously constructed isopro object to assess anomalous observations in the test data. By default, returns quantile values representing the depth of each test observation relative to the original training data. Smaller values indicate greater outlyingness.

To return raw depth values instead of quantiles, set quantiles = FALSE.

Author(s)

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.

See Also

isopro uvarpro varpro

Examples

## ------------------------------------------------------------
##
## 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")

kogalur/varPro documentation built on June 2, 2025, 6:24 a.m.