An ensemble of Isolation Trees all of which 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. Anomalies are isolated into the shallowest leaf nodes and as such receive the highest isolation scores.
Data Type Compatibility: Categorical, Continuous
|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.|
use Rubix\ML\AnomalyDetectors\IsolationForest; $estimator = new IsolationForest(100, 0.2, 0.05);
This estimator does not have any additional methods.
- 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.