APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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more than 60%
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less than 60%
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more than 50%
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less than 50%
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Detailed explanation-1: -Note: Suppose each individual base models have accuracy greater than 50%. A lower correlation among ensemble model members will increase the error-correcting capability of the model. So it is preferred to use models with low correlations when creating ensembles.
Detailed explanation-2: -Ensemble model works better, when we ensemble models with low correlation. A good example of how ensemble methods are commonly used to solve data science problems is the random forest algorithm (having multiple CART models).
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.