Concept drift

In predictive analytics, the term concept refers to the quantity you are looking to predict. For example, in a fraud detection application the target concept may be a binary attribute FRAUDULENT with values "yes" or "no" that indicates whether a given transaction is fraudulent. Or, in a weather prediction application, there may be several target concepts such as TEMPERATURE, PRESSURE, and HUMIDITY.

More generally, the term concept can also refer to other phenomena of interest other than the target concept, such as an input, but in the context of concept drift we are specifically talking about the target concept.

The term concept drift, then, refers to unforeseen changes over time in the phenomenon of interest. For example, online shopping behaviours change over time. Let's say you want to predict weekly merchandise sales, and you have developed a predictive model that works to your satisfaction. The model may use inputs such as the amount of money spent on advertising, promotions you are running, and other metrics that may affect sales. What you are likely to experience is that the model will become less and less accurate over time - you will be a victim of concept drift. In the merchandise sales application, one reason for concept drift may be seasonality, which means that shopping behaviour changes seasonally. You will likely have higher sales in the winter holiday season than during the summer.