In case of limited training data, which technique, bagging or stacking, would be preferred, and why?
A. Bagging, because we can combine as many classifier as we want by training each on a different sample of the training data
B. Bagging, because we use the same classification algorithms on all samples of the training data
C. Stacking, because each classifier is trained on all of the available data
D. Stacking, because we can use different classification algorithms on the training data
Answer:C
In case of limited training data, which technique, bagging or stacking, would be preferred, and why?
A. Bagging, because we can combine as many classifier as we want by training each on a different sample of the training data B. Bagging, because we use the same classification algorithms on all samples of the training data C. Stacking, because each classifier is trained on all of the available data D. Stacking, because we can use different classification algorithms on the training data