You are training a supervised learning model and notice that training performance is much better than validation performance. You want the model to generalize better to unseen data.
How would you handle overfitting in a model?
High training score, weaker validation or test scoreLarge gap between train and validation AUC or lossPerformance degrades after adding model complexityUnstable results across folds