📌 Drift Scores Summary
Drift scores are numerical values that measure how much data has changed over time compared to a previous dataset. They help identify shifts or changes in the patterns, distributions, or characteristics of data. These scores are often used to monitor whether data used by a machine learning model is still similar to the data it was originally trained on.
🙋🏻♂️ Explain Drift Scores Simply
Imagine you have a recipe for biscuits, and you always use the same ingredients. If you start noticing that the biscuits taste different, a drift score would tell you how much the ingredients or the final product have changed. It is like a change detector that helps you spot when things are not the same as before.
📅 How Can it be used?
Drift scores can alert teams when data feeding a fraud detection model starts to differ from the data used during model training.
🗺️ Real World Examples
A bank uses drift scores to monitor transaction data for its fraud detection system. If customers start making purchases in very different ways than before, the drift score will rise, signalling the need to retrain or adjust the model to maintain accuracy.
An online retailer tracks drift scores for user behaviour data on their website. When customer navigation patterns change after a site redesign, a high drift score warns the analytics team that their recommendation algorithms may need updating.
✅ FAQ
What is a drift score and why does it matter?
A drift score is a number that tells you how much your data has changed compared to what it looked like before. This is important because if your data changes a lot, any predictions or decisions made by your model might not be as reliable as they once were.
How can drift scores help keep my machine learning model accurate?
Drift scores make it easier to spot when your data starts to look different from what your model was trained on. By checking these scores regularly, you can catch changes early and update your model if needed, helping it stay accurate and trustworthy.
When should I check drift scores in my data?
It is a good idea to check drift scores whenever you get new batches of data or before using your model to make important decisions. This helps you notice if your data has shifted and whether your model might need a refresh.
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