Gaussian MLE#
The Gaussian Maximum Likelihood Estimator (MLE) is able to spot outliers by computing a probability density function (PDF) over the features assuming they are independently and normally (Gaussian) distributed. Samples that are assigned low probability density are more likely to be outliers.
Interfaces: Estimator, Learner, Online, Scoring, Persistable
Data Type Compatibility: Continuous
Parameters#
# | Name | Default | Type | Description |
---|---|---|---|---|
1 | contamination | 0.1 | float | The proportion of outliers that are assumed to be present in the training set. |
2 | smoothing | 1e-9 | float | The amount of epsilon smoothing added to the variance of each feature. |
Example#
use Rubix\ML\AnomalyDetectors\GaussianMLE;
$estimator = new GaussianMLE(0.03, 1e-8);
Additional Methods#
Return the column means computed from the training set:
public means() : float[]
Return the column variances computed from the training set:
public variances() : float[]
References#
-
T. F. Chan et al. (1979). Updating Formulae and a Pairwise Algorithm for Computing Sample Variances. ↩
Last update:
2021-03-27