APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Classifiers that are more “sure” can vote with more conviction
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Classifiers can be more “sure” about a particular part of the space
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Most of the times, it performs better than a single classifier
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All of the above
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Detailed explanation-1: -What is true about an ensembled classifier? In an ensemble model, we give higher weights to classifiers which have higher accuracies. In other words, these classifiers are voting with higher conviction. On the other hand, weak learners are sure about specific areas of the problem.
Detailed explanation-2: -Which of the following statement is NOT True about ensemble methods? ANSWER: The correct option is : OPTION C) : Ensemble methods may include bagging, boosting, random forest and hyperparameter tuning Reason: Ensemble methods include bagging, boostin…
Detailed explanation-3: -There are two main reasons to use an ensemble over a single model, and they are related; they are: Performance: An ensemble can make better predictions and achieve better performance than any single contributing model. Robustness: An ensemble reduces the spread or dispersion of the predictions and model performance.