The Gaussian Maximum Likelihood Estimator (MLE) is able to spot outliers by computing a probability density function over the features assuming they are independent and normally (Gaussian) distributed. Assigning low probability density translates to a high anomaly score. The final anomaly score is given as the log likelihood of a sample being an outlier.
Data Type Compatibility: Continuous
|1||threshold||5.0||float||The minimum log likelihood to be flagged as an anomaly.|
|2||contamination||0.1||float||The percentage of outliers that are assumed to be present in the training set.|
Return the column means computed from the training set:
public means() : array
Return the column variances computed from the training set:
public variances() : array
use Rubix\ML\AnomalyDetection\GaussianMLE; $estimator = new GaussianMLE(6.0, 0.1);
- T. F. Chan et al. (1979). Updating Formulae and a Pairwise Algorithm for Computing Sample Variances.