Got a classification problem but your dataset has way more samples from one class than the other? Imbalanced datasets can mess with your model's performance pretty badly. Been playing around with some 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 facing heavy class imbalance?
Effective ways to handle imbalanced ML datasets?
👁️ 88 görüntüleme💬 1 cevap❤️ 0 beğeni
1 Cevap
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?