Quick anomaly detection

A simple wrapper function to quickly identify anomalies using contamination rate

Usage

is_anomaly(object, contamination = 0.05)

Arguments

object

An isoForest model object

contamination

The expected proportion of outliers. Default is 0.05 (5%)

Value

A logical vector indicating which samples are anomalies

Examples

# Train model and detect anomalies
model <- isoForest(iris[1:4])
anomalies <- is_anomaly(model, contamination = 0.05)

# Show anomalous samples
iris[anomalies, ]
#>     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
#> 16           5.7         4.4          1.5         0.4    setosa
#> 33           5.2         4.1          1.5         0.1    setosa
#> 42           4.5         2.3          1.3         0.3    setosa
#> 110          7.2         3.6          6.1         2.5 virginica
#> 115          5.8         2.8          5.1         2.4 virginica
#> 118          7.7         3.8          6.7         2.2 virginica
#> 119          7.7         2.6          6.9         2.3 virginica
#> 132          7.9         3.8          6.4         2.0 virginica