Cheng-tao explains how class imbalance affects machine learning models and the importance of aligning the loss function with business objectives. Techniques like oversampling and penalty metric adjustments are discussed to tackle class imbalance, crucial for scenarios like fraud detection where misalignment can lead to inaccurate results.
