Hyperparameter tuning for outlier detection

For the more advanced user, the Data Frame Analytics wizard offers an opportunity to configure and tune hyperparameters – various knobs and dials that fine-tune how the outlier detection algorithm works. The available hyperparameters are displayed in Figure 10.17. For example, we can direct the outlier detection job to use only a certain type of outlier detection method instead of the ensemble, to use a certain value for the number of nearest neighbors that are used in the computation in the ensemble, and to assume that a certain portion of the data is outlying.

Please note that while it is good to play around with these settings to experiment and get a feel for how they affect the final results, if you want to customize any of these for a production usecase, you should carefully study the characteristics of your data and have an awareness of how these characteristics will interact with your chosen hyperparameter settings. More...