Information Gain in Decision Trees

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What is the entropy of a group in which all examples belong to the same class?

0 (correct)

1

Undefined

Maximum

What is the entropy of a group with 50% in either class?

0.5

Undefined

1 (correct)

0

Which of the following best describes the goal of information gain?

To determine the most useful attribute for discriminating between classes (correct)

To calculate the average message length of a Huffman code

To minimize the uncertainty in the class distribution

To maximize the uncertainty in the class distribution

In the Huffman coding example provided, what is the average message length?

1.75 Signup and view all the answers

What is the relationship between entropy and conditional entropy?

Entropy is the sum of conditional entropy and mutual information Signup and view all the answers

Which of the following is a key property of decision trees?

They greedily optimize for information gain at each split Signup and view all the answers

What does impurity measure in a group of examples?

The level of randomness or disorder in the group Signup and view all the answers

Which coding scheme assigns bits based on -log2P(X=i) to encode a message X?

David Huffman's code Signup and view all the answers

What does entropy measure for a random variable X?

The expected number of bits needed to encode a randomly drawn value of X Signup and view all the answers

In decision trees, what does Information Gain help determine?

The optimal split attribute for classification Signup and view all the answers

What is Mutual Information used for in machine learning?

To evaluate feature importance by measuring the relationship between variables Signup and view all the answers

What is the purpose of Information Gain in the context of decision trees?

To determine the order of attributes in the nodes of a decision tree Signup and view all the answers

What is the formula for calculating the entropy of a random variable X?

$H(X) = -\sum_{i=1}^{n} P(x_i) \log_2 P(x_i)$ Signup and view all the answers

What is the relationship between Information Gain and Mutual Information?

Information Gain is the same as Mutual Information Signup and view all the answers

What is the purpose of calculating the conditional entropy in the context of decision trees?

To calculate the entropy of a subset of the data after splitting on an attribute Signup and view all the answers

In the example shown, what is the value of the Information Gain when splitting on the attribute used in the root node?

0.38 Signup and view all the answers

Which of the following statements about entropy and information gain is true?

Entropy and information gain are inversely related Signup and view all the answers

Information Gain in Decision Trees

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