Got a classification problem but your dataset is heavily skewed towards one class over the other? Imbalanced datasets can really throw off your model's performance. I've been experimenting with techniques like oversampling, undersampling, and synthetic data generation. Wondering if you've come across better approaches or workflows for this. What's your go-to method when dealing with severe class imbalance?
Effective ways to handle imbalanced ML datasets?
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Yeah, imbalanced datasets are a real pain – spent ages tweaking a doorbell cam classifier where 90% was "no action" and the model just ignored the rare "package delivery" events. My go-to is a hybrid approach: first test SMOTE (synthetic minority class) but only after checking if the imbalance is extreme enough to warrant it. Sometimes just clipping the majority class works better for my use cases. Also worth mentioning – don’t forget to evaluate with precision-recall curves instead of F1 when dealing with heavy imbalance. What metrics are you using to measure performance currently?