Description
Imagine a courtroom where one side has a hundred lawyers, and the other only has one. It’s hardly a fair trial! Similarly, class imbalance occurs in AI when one class (majority class) dominates the dataset, leaving the minority class underrepresented. This can lead to biased models that perform poorly on the minority class, even with high overall accuracy.
Here is a peek into the critical class imbalance issue in AI model training. Plus, challenges it poses, the consequences of ignoring it, and the techniques you can employ to balance the scales and ensure fair and accurate learning for all classes.
Kognition.Info paid subscribers can download this and many other How-To guides. For a list of all the How-To guides, please visit https://www.kognition.info/product-category/how-to-guides/