COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Set the same seed value for each run
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Use multiple random initialization
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Both A and B
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None of the above
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Detailed explanation-1: -How can you prevent a clustering algorithm from getting stuck in bad local optima? C.K-Means clustering algorithm has the drawback of converging at local minima which can be prevented by using multiple radom initializations.
Detailed explanation-2: -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-3: -Which of the following machine learning algorithm is based upon the idea of bagging? Answer-B) Random forest is based on the idea of bagging. 9.
Detailed explanation-4: -Decision trees can also be used to perform clustering, with a few adjustments. On one hand, new split criteria must be discovered to construct the tree without the knowledge of samples la-bels. On the other hand, new algorithms must be applied to merge sub-clusters at leaf nodes into actual clusters.
Detailed explanation-5: -Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging? Explanation: The Radom Forest algorithm builds an ensemble of Decision Trees, mostly trained with the bagging method.