DATABASE FUNDAMENTALS
BASICS OF BIG DATA
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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1 and 3
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1 and 4
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2 and 3
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2 and 4
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Detailed explanation-1: -Which of the following is true about individual(Tk) tree in Random Forest? Random forest is based on bagging concept, that consider faction of sample and faction of feature for building the individual trees.
Detailed explanation-2: -QUESTION 1 Which of the following statements is true about random forests Random forests are an ensemble method. They combine and average the predictions from large number of trees.
Detailed explanation-3: -Which of the following is/are true about Random Forest and Gradient Boosting ensemble methods? Both algorithms are design for classification as well as regression task.
Detailed explanation-4: -How does the Random Forest algorithm work? Random Forest grows multiple decision trees which are merged together for a more accurate prediction. The logic behind the Random Forest model is that multiple uncorrelated models (the individual decision trees) perform much better as a group than they do alone.