Understanding model drift and decay
Just like a river that changes its course over time, models in ML can experience drift and decay. Now, you might be wondering, what does this mean? Let’s delve into it. Model drift refers to when our ML model’s performance degrades over time due to changes in the underlying data it was trained on or due to changes in the problem space itself.
As we know, ML models are not set in stone. They are designed to adapt and learn from new information. However, when the dynamics of the input data or the patterns that were initially recognized start to shift, our models might fail to adapt swiftly enough. This is where we encounter the problem of model drift.
Model drift
Now, there are several types of model drift we should be aware of. Each tells a different tale of how our models can falter:
- The first type is concept drift. Think of a sentiment analysis (SA) algorithm. Over time, the way people use certain words or phrases...
