When working with classification models, we risk underfitting when the decision boundary is too simple and fails to effectively separate different classes. Conversely, overfitting occurs when the decision boundary is too complex, perfectly classifying the training data but performing poorly on the test set. The goal is to find a balance—where the model generalizes well and provides consistent separation across both training and testing data.

What challenges have you encountered when working with classification problems?