COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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RF completely a different machine learning technique
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RF combines multiple decision trees to make a decision
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RF cannot handle continuous data
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None of the above
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Detailed explanation-1: -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-2: -Random forest is an ensemble of many decision trees. Random forests are built using a method called bagging in which each decision trees are used as parallel estimators. If used for a classification problem, the result is based on majority vote of the results received from each decision tree.
Detailed explanation-3: -Expert-Verified Answer (B) The entropy of a node typically decreases as we go down a decision tree is true about a decision tree.
Detailed explanation-4: -The random forest is a classification algorithm consisting of many decisions trees. It uses bagging and feature randomness when building each individual tree to try to create an uncorrelated forest of trees whose prediction by committee is more accurate than that of any individual tree.