Outlier detection models
The Outlier Detection API is experimental and may change in future releases of MLJ.
Outlier detection or anomaly detection is predominantly an unsupervised learning task, transforming each data point to an outlier score quantifying the level of "outlierness". However, because detectors can also be semi-supervised or supervised, MLJModelInterface provides a collection of abstract model types, that enable the different characteristics, namely:
MLJModelInterface.SupervisedDetectorMLJModelInterface.UnsupervisedDetectorMLJModelInterface.ProbabilisticSupervisedDetectorMLJModelInterface.ProbabilisticUnsupervisedDetectorMLJModelInterface.DeterministicSupervisedDetectorMLJModelInterface.DeterministicUnsupervisedDetector
All outlier detection models subtyping from any of the above supertypes have to implement MLJModelInterface.fit(model, verbosity, X, [y]). Models subtyping from either SupervisedDetector or UnsupervisedDetector have to implement MLJModelInterface.transform(model, fitresult, Xnew), which should return the raw outlier scores (<:Continuous) of all points in Xnew.
Probabilistic and deterministic outlier detection models provide an additional option to predict a normalized estimate of outlierness or a concrete outlier label and thus enable evaluation of those models. All corresponding supertypes have to implement (in addition to the previously described fit and transform) MLJModelInterface.predict(model, fitresult, Xnew), with deterministic predictions conforming to OrderedFactor{2}, with the first class being the normal class and the second class being the outlier. Probabilistic models predict a UnivariateFinite estimate of those classes.
It is typically possible to automatically convert an outlier detection model to a probabilistic or deterministic model if the training scores are stored in the model's report. Below mentioned OutlierDetection.jl package, for example, stores the training scores under the scores key in the report returned from fit. It is then possible to use model wrappers such as OutlierDetection.ProbabilisticDetector to automatically convert a model to enable predictions of the required output type.
OutlierDetection.jl provides an opinionated interface on top of MLJ for outlier detection models, standardizing things like class names, dealing with training scores, score normalization and more.