DATABASE FUNDAMENTALS
BASICS OF BIG DATA
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Reason I
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Reason II
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Both the Reasons
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None of the Reasons
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Detailed explanation-1: -26) When you use the boosting algorithm you always consider the weak learners. Which of the following is the main reason for having weak learners? To prevent overfitting, since the complexity of the overall learner increases at each step.
Detailed explanation-2: -Which of the following is/are true about boosting trees? In boosting tree individual weak learners are not independent of each other because each tree correct the results of previous tree. Bagging and boosting both can be consider as improving the base learners results.
Detailed explanation-3: -Two approaches to avoiding overfitting are distinguished: pre-pruning (generating a tree with fewer branches than would otherwise be the case) and post-pruning (generating a tree in full and then removing parts of it). Results are given for pre-pruning using either a size or a maximum depth cutoff.
Detailed explanation-4: -Types of Boosting Algorithms AdaBoost (Adaptive Boosting) Gradient Tree Boosting. XGBoost.