When fitting regression models to predict continuous variables, we often face challenges with underfitting and overfitting. Underfitting occurs when the model is too simple and fails to capture the underlying patterns in the data. On the other hand, overfitting happens when the model is overly complex, capturing intricate details of the training set but performing poorly on the testing set. Ideally, we aim for a balance—where the model generalizes well, capturing the overall trend in both the training and testing data.

What challenges have you encountered when fitting regression models?