Isolation Forest#
An ensemble anomaly detector comprised of Isolation Trees (ITrees) trained on a unique subset of the training set. Isolation Trees are a type of randomized decision tree that assign isolation scores based on the depth a sample reaches in the tree. Outliers are said to be isolated earliest in the growing process and therefore receive higher isolation scores. The Isolation Forest works by averaging the isolation scores of a sample across a user-specified number of trees.
Interfaces: Estimator, Learner, Ranking, Persistable
Data Type Compatibility: Categorical, Continuous
Parameters#
| # | Param | Default | Type | Description |
|---|---|---|---|---|
| 1 | estimators | 300 | int | The number of isolation trees to train in the ensemble. |
| 2 | contamination | null | float | The percentage of outliers that are assumed to be present in the training set. |
| 3 | ratio | 0.2 | float | The ratio of samples to train each estimator with. If null then subsample size is 256. |
Additional Methods#
This estimator does not have any additional methods.
Example#
use Rubix\ML\AnomalyDetection\IsolationForest;
$estimator = new IsolationForest(100, 0.2, 0.01);
$estimator = new IsolationForest(100); // Default sample size and threshold
References#
- F. T. Liu et al. (2008). Isolation Forest.
- F. T. Liu et al. (2011). Isolation-based Anomaly Detection.