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Retraining the Model in ThingWorx Analytics
When using ThingWorx Analytics Products to build Prediction Models, it is not enough to end up with models that are a Technical Success. The purpose is to ultimately have models that are a Business Success. What the user would want to achieve is to have Models that remain reliable and accurate in a potentially changing production environment.
Therefore, when your environment changes, the model that you have used and relied on might no longer provide the same quality of results. Hence the need to retrain your model.
Types of Models to be retrained:
There are currently two types of models that are created with ThingWorx Analytics:
Each of those models could require retraining based on the context in which they are created then used.
When to retrain your Model:
- Predictive models:
For predictive analytics models, the main initiator for retraining would be a change in the production environment. resulting in the change of collected Dataset. This could nonetheless be caused by many factors:
- Anomaly Detection models:
In anomaly detection, the need to retrain the models originates mainly from a change in what is considered to be a Normal behavior of a certain monitored property. The could be caused by the following factors:
This might not be an exhaustive list of the reasons that would require either a Predictive or an Anomaly Detection Model to be retrained.
As a general rule of thumb, if the model starts delivering results that are below expected or if the business context for the model is not valid, then it might be a wise decision to retrain the Analytics Model.