You've just finished training a random forest for spam classification, and it is getting abnormally bad performance on your validation set, but good performance on your training set. Your implementation has no bugs. What could be causing the problem? Your decision trees are too deep You are randomly sampling too many features when you choose a split You have too few trees in your ensemble Your bagging implementation is randomly sampling sample points without replacement

You've just finished training a random forest for spam classification, and it is getting abnormally bad performance on your validation set, but good performance on your training set. Your implementation has no bugs. What could be causing the problem? Your decision trees are too deep You are randomly sampling too many features when you choose a split You have too few trees in your ensemble Your bagging implementation is randomly sampling sample points without replacement