Description
Imagine training a dog to fetch. You throw the ball 100 times, but 99 times you just pretend to throw it. The dog might learn to “stay” really well, but its fetching skills will be abysmal! This is similar to what happens with imbalanced data in AI. If your dataset is dominated by one class, your model might become biased and fail to accurately predict the minority class – often the one you care about most.
Here are the strategies and techniques to effectively evaluate AI models trained on imbalanced data. From choosing the right metrics to resampling techniques and advanced methods, you’ll learn how to navigate this challenging landscape and ensure your model performs reliably across all classes.
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