Question at position 16 Multiple Choice Question: Entropy can be negativeis used to calculate information gainis a measure of information gainis a measure of correlation between numeric variables

Question

BlackTom AI · Accepted Answer

First, let's break down what entropy means in information theory and how it relates to information gain.
Option 1: 'can be negative' — Entropy is a measure of uncertainty and is defined to be nonnegative; in fact, H(X) ≥ 0 for any random variable X. Therefore, this statement is incorrect.
Option 2: 'is used to calculate information gain' — Information gain in decision trees is computed as the difference between the entropy (or impurity) of the parent node and the weighted sum of the entropies of the child nodes after a split. Thus, entropy is indeed used in calculating information gain, making this the correct statement.
Option 3: 'is a measure of information gain' — This misstates the relationship. Entropy itself quantifies uncertainty or impurity, not the gain obtained from a split. Information gain is derived from entropy but is a separate metric that measures the reduction in entropy due to a split. So this option is incorrect.
Option 4: 'is a measure of correlation between numeric variables' — Entropy is not a measure of correlation. Measures of correlation include Pearson or Spearman coefficients, while entropy relates to uncertainty and information content. This option is incorrect.
In summary, evaluating each statement shows that only the second option accurately describes entropy's role in the context of information gain, while the others either describe a different concept or are factually inaccurate.

Question at position 16 Multiple Choice Question: Entropy can be negativeis used to calculate information gainis a measure of information gainis a measure of correlation between numeric variables

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