COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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We can extract RULES from the Decision Tree
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It is good to find relationship of non-linearly correlated data
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it has to formed from a binary tree
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The output can be easily understood by a layman
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Detailed explanation-1: -Solution: E Decision trees doesn’t aggregate the results of multiple trees so it is not an ensemble algorithm.
Detailed explanation-2: -Binary search trees store data conveniently for searching later. Some bounds on worst case scenarios for searching and sorting are obtained. Definition: a decision tree is a tree in which • internal nodes represent actions, • arcs represent outcomes of an action, and • leaves represent final outcomes.
Detailed explanation-3: -Explanation: “A decision tree” is constructed with a top-down approach from a “root node” with the partitioning of the “data into subsets” compromising instances with homogenous similar values (homogeneous). A decision tree applies the predictive modeling method followed in statistics, data mining and machine learning.
Detailed explanation-4: -Expert-Verified Answer. (B) The entropy of a node typically decreases as we go down a decision tree is true about a decision tree.