Model Drift (also known as concept drift, shift, or concept change) is a phenomenon in machine learning which occurs when the predictive model that was used to make certain inferences or predictions becomes obsolete or out of date due to an external factor. For instance, a common example of model drift is a predictive model that predicts a certain trend. If the data used to train the model changes significantly over time, the model’s prediction may start to differ from the actual trend. As such, changes in the data over time can lead to model drift.
Model drift can occur for a variety of reasons. For example, changes in the data distribution, changes in the underlying process that generated the data, or changes in the environmental conditions can all lead to model drift. Another reason model drift can occur is due to the ever-increasing complexity of the data used to train predictive models. As the data used to train models become increasingly complex, predictive models can become less accurate over time.
In order to combat model drift, there are various techniques and concepts that have been developed. These include using dynamic learning algorithms which can adapt to the changing data environment, and using ensemble models to create models which are more robust and reliable. Additionally, regular maintenance of the models, monitoring of model performance, and validation on new data can all help mitigate model drift.
Overall, model drift is an important concept in machine learning to be aware of, and one which should be taken into account when building predictive models. By understanding the potential issues of model drift and taking proactive steps to mitigate it, machine learning models can remain accurate over the long-term.
