What is underfitting? How can you address it?

Underfitting happens when a model is too simple to capture the underlying pattern in the data. It performs poorly on both training and validation data.

• How to detect — both training and validation errors are high; the model never reaches a satisfactory loss even on training data.

• Causes — model with too little capacity, missing relevant features, excessive regularization, too few training epochs.

How to fix:

• Use a more complex model (deeper tree, more layers).

• Add more relevant features or interaction terms.

• Reduce regularization.

• Train longer (more epochs).

• Reduce noise in the data.