APPLICATION OF SUPERVISED LEARNING
MACHINE LEARNINGHARD QUESTIONS
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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In Decision Tree algorithms entropy of a given dataset is zero. This statement implies ____
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Further splitting is required
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Need some other information to decide splitting
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No further splitting is required
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None of the Mentioned
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Explanation:
Detailed explanation-1: -Entropy is measured between 0 and 1. (Depending on the number of classes in your dataset, entropy can be greater than 1 but it means the same thing, a very high level of disorder.
Detailed explanation-2: -Let’s consider a case when all observations belong to the same class; then entropy will always be 0. E=−(1log21) = 0. When entropy becomes 0, then the dataset has no impurity. Datasets with 0 impurities are not useful for learning.
Detailed explanation-3: -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.
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