Isolation Forest#
An ensemble of Isolation Trees that each specialize on a unique subset of the training set. Isolation Trees are a type of randomized decision tree that assign anomaly scores based on the depth a sample reaches when traversing the tree. Based on the premise that anomalies are isolated into their own nodes sooner, samples that receive high anomaly scores achieve the shallowest depth during traversal.
Interfaces: Estimator, Learner, Scoring, Persistable
Data Type Compatibility: Categorical, Continuous
Parameters#
# | Name | Default | Type | Description |
---|---|---|---|---|
1 | estimators | 100 | int | The number of isolation trees to train in the ensemble. |
2 | ratio | null | float | The ratio of samples to train each estimator with. If null, the subsample size will be set to 256. |
3 | contamination | null | float | The proportion of outliers that are assumed to be present in the training set. If null, the threshold anomaly score will be set to 0.5. |
Example#
use Rubix\ML\AnomalyDetectors\IsolationForest;
$estimator = new IsolationForest(100, 0.2, 0.05);
Additional Methods#
This estimator does not have any additional methods.
References#
Last update:
2021-03-27