COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Decision tree is an example of linear classifier.
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The entropy of a node typically decreases as we go down a decision tree.
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Entropy is a measure of purity
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An attribute with lower mutual information should be preferred to other attributes.
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Detailed explanation-1: -Explanation: Entropy helps to determine the impurity of a node and as we go down the decision tree, entropy decreases.
Detailed explanation-2: -A) Decision tree is an example of linear classifier. B) The entropy of a node typically decreases as we go down a decision tree. C) Entropy is a measure of purity.
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: -In the context of Decision Trees, entropy is a measure of disorder or impurity in a node. Thus, a node with more variable composition, such as 2Pass and 2 Fail would be considered to have higher Entropy than a node which has only pass or only fail.
Detailed explanation-5: -By using entropy, decision trees tidy more than they classify the data. In physics, the second law of thermodynamics states that the entropy always increases over time, if you don’t bring (or take) any energy to the system. Don’t wait too long before tidying your room or your data!