What is dataset drift?

What is dataset drift?

What is Concept Drift? Concept drift in machine learning and data mining refers to the change in the relationships between input and output data in the underlying problem over time. In other domains, this change maybe called “covariate shift,” “dataset shift,” or “nonstationarity.”

What is concept shift?

Dataset shift is not a standard term and is sometimes referred to as concept shift or concept drift, changes of classification, changing environments, contrast mining in classification learning, fracture points and fractures between data.

How do you calculate data Drifting?

There are two techniques to measure drift:

1. Statistical: This approach uses statistical metrics.
2. Model-based: This approach uses a model to determine the similarity between a given point or groups of points to a reference baseline.

How is concept drift and model decay detected?

Concept drift driven model decay can be expensive to fix given the need for manual re-labeling of old data. Drift can be detected via global measures such as worsening f1-scores for sampled new data. We could do a complete re-label of all data here as our labeling is not manual but rather formula driven.

What is the difference between concept drift and data drift?

Data drift While concept drift is about the target variable, there’s another phenomenon, named data drift, that describes the change of the properties of the independent variables. In this case, it is not the definition of a spammer that changes, but the values of the features we are using to define them.

How to detect concept drift in machine learning?

So the continual morphing of the data decision boundary is at the crux of concept drift. The only way to detect it then of course is to make the effort to label at least some of the new data on a routine basis and look for degradation in the predictive ability of the model.

How is data drift caused by unexpected events?

Data drift is also caused by unexpected events we can’t control. For example, suppose that your model is doing so well and catching so many spammers that at some point, they change their spamming behavior to try to fool the model.