How to Detect Concept Drift in Machine Learning

Machine learning (ML) is a powerful force fed by data to become more proficient at performing assigned tasks to execute predictive modeling. In conjunction with artificial intelligence (AI), the two could help humans create solutions never understood because of an extensive backlog of historical data and an infinite amount of novel, incoming information. There are sometimes inaccuracies or changes due to this volume, so what happens at that point?

What Is Concept Drift in ML?

Concept drift in ML is when outdated or inaccurate data influences the creation of predictive models. ML often generates its determinations based on mapping that doesn’t consider instances where past data could inaccurately represent future predictions.

These variables in knowledge are called hidden contexts, which are impossible to predict if behaviors are innately unpredictable. Startups are appearing in the tech sector to solve the hidden context issue. Examples of potential gaps in intellect include:

• Human driving behaviors, like ignoring right-of-way rules.
• Government spending in a volatile economy.
• Severe weather predictions during the climate crisis.

Therefore, analysts must find these discrepancies before influencing decisions and update them accordingly. The objective should be high scalability because systems degrade over time. If humans change the patterns, theoretical models adapt to build more accurately. Analysts must become experts in determining ML’s relationship with its data set better than it knows itself.

What Are the Types of Concept Drift?

Concept drift manifests in multiple ways. Understanding the various facades will allow analysts to respond more accurately to blips in the algorithms. The types include:

• Gradual concept drift: Changes like this usually have roots in human behavior. Spending, responding to cybersecurity breaches, and media consumption all shift gradually over time, making historical data obsolete in small steps.
• Recurring concept drift: ML may not accurately forecast events even if shifts are seasonally predictable. Though Black Friday happens every year, ML won’t be able to know the trends perfectly.
• Instantaneous concept drift: Unforeseen international events or global influence will provide countless outliers, such as the pandemic affecting work, travel, and shopping behaviors.

As data becomes more plentiful and complex in ML, other types of drift may be born — especially with the creativity and unpredictability of humanity.

What Are Detection and Assessment Methods?

The goal is to create a drift-aware system that uses forecasting of changes and prediction error analysis to detect anomalies. Alongside testing algorithms to simulate concept drift, like adaptive windowing, it should be simpler to find points of misdirection.

Analysts that detect anomalies have a few options to correct the data, so it doesn’t skew any more models. Most of it falls under the umbrella of adjusting back data, updating it to account for weight and importance, or improving it for accuracy.

Another option is to incorporate expected changes ML cannot detect into the data. Analysts that discover a learned difference can implement this knowledge to improve ML’s accuracy. Adversely, it could also confuse it more.

Online learning helps prevent concept drift because it allows the ML entity to update as it receives data samples. This is the most viable option for avoiding concept drift in real-time.

Minimizing Concept Drift in ML

Decreasing concept drift in ML is possible and becomes easier the more analysts understand human behavior. As ML develops, humans may engineer a way to eliminate concept drift, but that is unknown. By manually adjusting data sets, ML understands humanity more profoundly and accurately to perform better cybersecurity, create solutions for complex problems and develop more holistic perspectives about the world.