It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways.
If they deviate from the statistical properties of the training data set, then the learned predictions may become invalid, if the drift is not addressed.
The model may use inputs such as the amount of money spent on advertising, promotions being run, and other metrics that may affect sales.
Concept drift generally occurs when the covariates that comprise the data set begin to explain the variation of your target set less accurately — there may be some confounding variables that have emerged, and that one simply cannot account for, which renders the model accuracy to progressively decrease with time.
Generally, it is advised to perform health checks as part of the post-production analysis and to re-train the model with new assumptions upon signs of concept drift.
To prevent deterioration in prediction accuracy because of concept drift, reactive and tracking solutions can be adopted.
Reactive solutions retrain the model in reaction to a triggering mechanism, such as a change-detection test,[9][10] to explicitly detect concept drift as a change in the statistics of the data-generating process.
When concept drift is detected, the current model is no longer up-to-date and must be replaced by a new one to restore prediction accuracy.
By providing information about the time of the year, the rate of deterioration of your model is likely to decrease, but concept drift is unlikely to be eliminated altogether.