You are training a spam classifier. You notice that you are overfitting the training data. Which three actions can you take to resolve this problem? (Choose three.)
A. Get more training examples
B. Reduce the number of training examples
C. Use a smaller set of features
D. Use a larger set of features
E. Increase the regularization parameters
F. Decrease the regularization parameters
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the tools to prevent overfitting: less variables, regularization, early ending on the training…
A,C,E
Overfitting means that the classifier know too well the data and fails to generalize. We should use a smaller number of features to help the classifier generalize, and more examples so that it can have more variety.
> The gap in errors between training and test suggests a high variance problem in which the algorithm has overfit the training set. Increasing the regularization parameter will reduce overfitting and help with the variance problem.