What is model drift detection and why is it important?

What is model drift detection and why is it important?

Model drift detection refers to the process of identifying when a machine learning model's performance degrades over time due to changes in the underlying data distribution. It's important because it helps maintain the accuracy and reliability of predictions.

How can I detect model drift in my machine learning models?

You can detect model drift by monitoring performance metrics, comparing predictions to actual outcomes, and using statistical tests to identify shifts in data distribution.

What tools are available for model drift detection?

There are several tools available, such as Alibi Detect, Evidently AI, and NannyML, which provide functionalities for monitoring and detecting model drift.

Can you give me an example of model drift?

Sure! An example of model drift is a credit scoring model that was trained on data from a stable economic period. If a recession occurs, the model may no longer accurately predict creditworthiness due to changes in consumer behavior.