The correct answer is: True.
An ensemble of classifiers is a set of classifiers that are combined to produce a single prediction. The individual classifiers in an ensemble are often called “base learners”. There are many different ways to combine the predictions of the base learners, and the choice of combination method can have a significant impact on the accuracy of the ensemble.
In general, ensembles can be more accurate than any of their individual base learners. This is because the different base learners can make different mistakes, and by combining their predictions, the ensemble can often correct for these mistakes. However, there are also cases where an ensemble can be less accurate than its individual base learners. This can happen if the base learners are not very accurate to begin with, or if the combination method is not very effective.
Overall, ensembles can be a powerful tool for improving the accuracy of machine learning models. However, it is important to choose the right combination method and to train the ensemble on a large enough dataset to ensure that it is accurate.
Here are some additional details about each option:
- Option A: True. Ensembles of classifiers can be more accurate than any of their individual models. This is because the different base learners can make different mistakes, and by combining their predictions, the ensemble can often correct for these mistakes.
- Option B: False. Ensembles of classifiers may or may not be more accurate than any of their individual models. This depends on the choice of combination method and the training data.