Mastering Machine Learning with Limited Data: A Guide to Effective Model Training

This discussion provides a valuable framework for machine learning practitioners working with constrained computational resources. It emphasizes the importance of proper sampling techniques and validation strategies when training models on imbalanced datasets. This approach ensures robust model performance even when full datasets are inaccessible.