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Therefore, that CLT-based anomaly detection was necessary mainly for the first two cases. In my case, the data processing pipeline wasn’t definitive yet and even if the model’s re-training frequency had already been inferred, I wanted to monitor it on current data. Anomalous does not necessarily mean incoherent. For instance, if there is an economic crisis, most observations will have a sharp increase in their default score because their health has declined. Handling anomaliesĪnomalies can happen, among multiple reasons, because : - There is an anomaly in the data processing pipeline - The model is unstable or has to be re-trained - There is an external factor. A previous data analysis suggested that the model only had to be re-trained every year, but this anomaly analysis suggests that this should rather happen after 4 months. We can see from both the histogram and the line plot that the model shows signs of degradation after June 2021. Here, I kept a copy of my model and didn’t train it at all for several months.
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