Description
Imagine a jury where one voice dominates, drowning out the perspectives of others. This is what happens in the world of AI when datasets are imbalanced. One class significantly outweighs the others, leading to biased models that fail to recognize the nuances of the under-represented groups. Think fraud detection, where fraudulent transactions are rare but critical to identify.
Here are the techniques and strategies to tackle imbalanced datasets and build fairer, more accurate AI models. Plus the challenges, the consequences, and the solutions, empowering you to create AI systems that truly represent all voices in your data.
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