COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Each weak learner has equal say in the final decision
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Sequence in which each weak learner is created does not matter
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None of the above
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None of the above
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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: -Q17) Which of the following is not possible in a boosting algorithm? Increase in training error.
Detailed explanation-3: -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-4: -Each tree is called a “weak learner” for their high bias. XGBoost starts by creating a first simple tree which has poor performance by itself. It then builds another tree which is trained to predict what the first tree was not able to, and is itself a weak learner too.