applicable (version 0.1.0)

apd_isolation: Fit an isolation forest to estimate an applicability domain.

Description

apd_isolation() fits an isolation forest model.

Usage

apd_isolation(x, ...)
# S3 method for default
apd_isolation(x, ...)
# S3 method for data.frame
apd_isolation(x, ...)
# S3 method for matrix
apd_isolation(x, ...)
# S3 method for formula
apd_isolation(formula, data, ...)
# S3 method for recipe
apd_isolation(x, data, ...)

Value

A apd_isolation object.

Arguments

x

Depending on the context:

• A data frame of predictors.

• A matrix of predictors (see the categ_cols argument of isotree::isolation.forest()).

• A recipe specifying a set of preprocessing steps created from recipes::recipe().

...

Options to pass to isotree::isolation.forest(). Options should not include data.

formula

A formula specifying the predictor terms on the right-hand side. No outcome should be specified.

data

When a recipe or formula is used, data is specified as:

• A data frame containing the predictors.

Details

In an isolation forest, splits are designed to isolate individual data points. The tree construction process takes random split locations on randomly selected predictors. As splits are made in the tree, the algorithm tracks when data points are isolated as more splits are made. The first points that are isolated are thought to be outliers or anomalous. From these results, an anomaly score can be constructed.

This function creates an isolation forest on the training set and measures the reference distribution of the scores when re-predicting the training set. When scoring new data, the raw anomaly score is produced along with the sample's corresponding percentile of the reference distribution.

References

Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. "Isolation forest." 2008 Eighth IEEE International Conference on Data Mining. IEEE, 2008. Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. "Isolation-based anomaly detection." ACM Transactions on Knowledge Discovery from Data (TKDD) 6.1 (2012): 3.

Examples

if (FALSE) { # interactive()
if (rlang::is_installed(c("isotree", "modeldata"))) {
  library(dplyr)

  data(cells, package = "modeldata")

  cells_tr <- cells %>% filter(case == "Train") %>% select(-case, -class)
  cells_te <- cells %>% filter(case != "Train") %>% select(-case, -class)

  if_mod <- apd_isolation(cells_tr, ntrees = 10, nthreads = 1)
  if_mod
}
}