An ensemble learner comprised of Isolation Trees that are each trained 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 from root to leaf node. Anomalies are isolated into their own nodes earliest during tree traversal and therefore receive the highest isolation scores. The Isolation Forest works by averaging the anomaly scores for an unknown sample across a user-specified number of trees.
Data Type Compatibility: Categorical, Continuous
|1||estimators||100||int||The number of isolation trees to train in the ensemble.|
|2||ratio||float||The ratio of samples to train each estimator with. If
|3||contamination||Auto||float||The proportion of outliers that are presumed to be present in the training set.|
This estimator does not have any additional methods.
use Rubix\ML\AnomalyDetectors\IsolationForest; $estimator = new IsolationForest(100, 0.2, 0.03); $estimator = new IsolationForest(100); // Default sample size and threshold
- F. T. Liu et al. (2008). Isolation Forest.
- F. T. Liu et al. (2011). Isolation-based Anomaly Detection.
- M. Garchery et al. (2018). On the influence of categorical features in ranking anomalies using mixed data.