1<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/AnomalyDetectors/RobustZScore.php">[source]</a></span> 2 3# Robust Z-Score 4A 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. 5 6!!! note 7 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. 8 9**Interfaces:** [Estimator](../estimator.md), [Learner](../learner.md), [Scoring](../scoring.md), [Persistable](../persistable.md) 10 11**Data Type Compatibility:** Continuous 12 13## Parameters 14| # | Name | Default | Type | Description | 15|---|---|---|---|---| 16| 1 | threshold | 3.5 | float | The minimum Z-Score to be flagged as an anomaly. | 17| 2 | alpha | 0.5 | float | The weight of the maximum per-sample Z-Score in the overall anomaly score. | 18 19## Example 20```php 21use Rubix\ML\AnomalyDetectors\RobustZScore; 22 23$estimator = new RobustZScore(3.0, 0.3); 24``` 25 26## Additional Methods 27Return the median of each feature column in the training set: 28```php 29public medians() : float[]|null 30``` 31 32Return the median absolute deviation (MAD) of each feature column in the training set: 33```php 34public mads() : float[]|null 35``` 36 37## References 38[^1]: B. Iglewicz et al. (1993). How to Detect and Handle Outliers.