Description
Product Category: Baker’s Dozen
Format: PDF
A Baker’s Dozen Approaches for Handling Imbalanced Data in AI
Imbalanced datasets present one of the most significant challenges in enterprise AI applications, from fraud detection to rare event prediction. When one class significantly outnumbers others, models tend to be biased toward the majority class, potentially missing critical minority cases. Here are thirteen proven approaches to effectively handle imbalanced data, ensuring your AI models maintain high performance across all classes, regardless of their distribution in the training data.
The Product is available for download for our paid subscribers. The product is a PDF with 13 ideas elaborating each concept along with a short introduction, takeaways, and next steps. There are over 120 such downloadable Baker’s Dozen products.
For a comprehensive list, go to https://www.kognition.info/bakers-dozen-strategies-for-enterprise-ai/
