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 regularize a model to reduce overfitting?
Recognizing overfitting from train versus validation behaviorChoosing appropriate regularization methods for different model familiesUsing cross-validation to tune regularization strengthExplaining the bias-variance tradeoff clearly