Robust Z-Score#
A statistical anomaly detector that uses modified Z-Scores which are robust to preexisting outliers in the training set. The modified Z-Score uses the median and median absolute deviation (MAD) unlike the mean and standard deviation of a standard Z-Score - which are more sensitive to outliers. Anomalies are flagged if their final weighted Z-Score exceeds a user-defined threshold.
Note: An alpha value of 1 means the estimator only considers the maximum absolute Z-Score, whereas a setting of 0 indicates that only the average Z-Score factors into the final score.
Interfaces: Estimator, Learner, Ranking, Persistable
Data Type Compatibility: Continuous
Parameters#
| # | Param | Default | Type | Description |
|---|---|---|---|---|
| 1 | threshold | 3.5 | float | The minimum Z-Score to be flagged as an anomaly. |
| 2 | alpha | 0.5 | float | The weight of the maximum per-sample Z-Score in the overall anomaly score. |
Additional Methods#
Return the median of each feature column in the training set:
public medians() : ?array
Return the median absolute deviation (MAD) of each feature column in the training set:
public mads() : ?array
Example#
use Rubix\ML\AnomalyDetectors\RobustZScore;
$estimator = new RobustZScore(3.0, 0.3);
References#
- B. Iglewicz et al. (1993). How to Detect and Handle Outliers.