Every machine learning model is built on a set of assumptions that were correlated with data. However, your ML system might be incorrect because your assumptions have changed. This change is what we call Concept drift. You might have trained your machine learning algorithm to predict a certain outcome, but maybe that outcome is slowly shifting over time. You might also have a shift in the data that is feeding that outcome.
This so-called data drift is also another problem that can impact how accurate your models are. You might even have built your machine learning algorithm on faulty assumptions, meaning that every decision it makes could be wrong. Because of this, you must understand the concept of drift and how to build Concept drift detection into your ML system.
What Is Concept Drift?
As mentioned above, Concept drift refers to how accurate our model is when the resulting assumptions it was created on have changed. The context in which you build the model change, meaning that it is no longer appropriate at making the current decisions for what is now the reality. The difference between the expected reality and the actual reality is called Concept drift. It is a crucial part of the machine learning model development process, meaning that your ML system will have to take these changes into consideration. This makes it all the more difficult to create accurate machine learning models that can stand the test of time.
You also have to factor in the changes that will come from your business environment being a lot different than what you thought. There are three different types of Concept drift that can happen. For example, you can have a sudden drift because something new and groundbreaking has just happened. You can also have seasonal drift that happens at a set time every time. Finally, you can have incremental drift because certain things slowly change over time.
Knowing When You Have It
It is crucial to have a monitoring solution in place to know when data drift and Concept drift have happened. A huge part of that system is having a good understanding of the type of drift that has occurred. Was it sudden? Or was it incremental or recurring? These are all factors in your ML system that make the process of concept drift detection easier.
As time goes on, these systems that help you monitor how drift has impacted your model will be crucial components in building sound and secure machine learning systems. An end-to-end system from xpresso.ai is one of the many ways you can solve the problem of drift monitoring and detection. By having this system, you no longer have to worry about the ins and outs of doing the monitoring yourself. You can then focus on incrementally improving your model to beat Concept drift and data drift.
Making Your System Aware of Concept and Data Drift
A simple way of ensuring that you take Concept drift and data drift into consideration is to retrain your model. That means you take the data that has shifted and run it through your algorithm again. You can adapt your model to the current reality, making it more accurate at predicting outcomes. When you combine this with good monitoring, you end up with a system that can minimize the impact that data drift and Concept drift will have on your ML system. This built-in Concept drift detection works extremely well, and it is something you can try in your own applications.