Machine Learning MCQ - Bagging or stacking - which is best for small training data?

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Machine Learning MCQ - Bagging vs stacking - Which ensemble method is best suited for small training data?

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1. 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) Stacking, because each classifier is trained on all of the available data When data is at a premium, we would ideally prefer to train all models on all of the available training data. Stacking methods use a separate model (a meta learner) to combine the results of the base models (weak learners or constituent models). The second difference is that the stacking based models are mostly heterogeneous in nature as they tend to train different kinds (algorithmically different) of base models. Meta learner takes the outputs of base models as input and gives the prediction as to the final output.   What is bagging in machine learning? Bagging is the type of ensemble technique in which a single training algorithm is used on different subsets of the training data where the subset sampling is done with replacement (bootstrap). Once the algorithm is trained on all the subsets, then bagging predicts by aggregating all the predictions made by the algorithm on different subsets.   What is ensemble learning? Ensemble learning is a machine learning paradigm where multiple models (often called “weak learners” or base models) are trained to solve the same problem and combined to get better results. The main hypothesis is that when weak models are correctly combined, we can obtain more accurate and/or robust models.   Three ensemble learning methods Bagging – use of homogeneous weak learners in parallel to learn and then average them for final predictions Boosting – use of homogeneous weak learners in sequential manner to learn and tries to reduce bias on final predictions Stacking – different weak learners are fitted independently from each other and a meta-model is trained on top of that to predict outputs based on the outputs returned by the base models.   What is weak learner? A ‘weak learner’ is any ML algorithm (for regression/classification) that provides accuracy slightly better than random guessing.

 

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