Data Imbalance
Data imbalance occurs when the number of samples for one class or category in a dataset is significantly higher or lower than others. This disparity can mislead AI models into prioritizing the majority class and performing poorly on the minority class. Addressing data imbalance often involves techniques like oversampling the minority class, undersampling the majority class, or using specialized algorithms.
Imagine teaching a student about animals, but showing them 100 pictures of cats and only 2 pictures of dogs, leading them to mostly recognize cats.
It's crucial for building AI models that perform fairly and accurately across all important categories, especially in critical applications like fraud detection or medical diagnosis.
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