What is concept drift?
Concept drift occurs when there are changes in the distribution of the training set examples.
At the most basic level, concept drift causes data points that were once considered an example of one concept to be seen as another concept entirely over time.
For instance, fraud detection models are at risk for concept drift when the concept of fraud is constantly changing.
This can cause model performance to degrade, especially over extended periods where concept drift continues to occur without being detected by your monitoring systems.
A critical problem for companies when integrating machine learning in their business processes is not knowing why they don’t perform well after a while. The reason is called concept drift. Here’s an informational guide to understanding the concept well.
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