How to Tame the Long Tail in Machine Learning

The long tail problem in machine learning describes the challenge of training models on imbalanced datasets where many classes have very few examples. This data distribution can cause models to perform poorly on rare but critical edge cases. Effectively managing the long tail is crucial for building robust and reliable AI systems, particularly in fields like autonomous vehicles or fraud detection. Common strategies to address this include targeted data sourcing, data augmentation, and using specialized model architectures or loss functions.