[source] # Robust Z-Score A statistical anomaly detector that uses modified Z-Scores that are robust to preexisting outliers in the training set. The modified Z-Score is defined as the feature value minus the median over the median absolute deviation (MAD). Anomalies are flagged if their final weighted Z-Score exceeds a user-defined threshold. !!! note A beta 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](../estimator.md), [Learner](../learner.md), [Scoring](../scoring.md), [Persistable](../persistable.md) **Data Type Compatibility:** Continuous ## Parameters | # | Name | Default | Type | Description | |---|---|---|---|---| | 1 | threshold | 3.5 | float | The minimum Z-Score to be flagged as an anomaly. | | 2 | beta | 0.5 | float | The weight of the maximum Z-Score in the overall anomaly score. | | 3 | smoothing | 1e-9 | float | The amount of epsilon smoothing added to the MAD of each feature. | ## Example ```php use Rubix\ML\AnomalyDetectors\RobustZScore; $estimator = new RobustZScore(3.5, 0.25, 1e-6); ``` ## Additional Methods Return the median of each feature column in the training set: ```php public medians() : float[]|null ``` Return the median absolute deviation (MAD) of each feature column in the training set: ```php public mads() : float[]|null ``` ## References [^1]: B. Iglewicz et al. (1993). How to Detect and Handle Outliers.