You're training a supervised learning model and notice it performs much better on the training set than on validation data. You want to improve generalization without throwing away useful signal.
How would you handle overfitting in a model?
Recognizing overfitting from train versus validation behaviorUsing regularization and model complexity controlApplying cross-validation correctlyTuning hyperparameters to improve generalization