APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Which is true about ensembles?
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Reduce variance error
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Reduce bias error
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Surely, reduce variance error
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none of these
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Explanation:
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.
Detailed explanation-2: -Bagging or Bootstrap Aggregation is a parallel ensemble learning technique to reduce the variance in the final prediction.
Detailed explanation-3: -Ensemble methods are ideal for reducing the variance in models, thereby increasing the accuracy of predictions. The variance is eliminated when multiple models are combined to form a single prediction that is chosen from all other possible predictions from the combined models.
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