Which attribute selection measure is used to calculate the reduction in entropy?Answer areaGini IndexInformation GainGain RatioChi-Square

The attribute selection measure used to calculate the reduction in entropy is Information Gain.

Here's a step-by-step explanation:

1. Entropy is a measure of the impurity, disorder or uncertainty in a set of data. It is used in decision trees to determine where to split the data.

2. Information Gain is a statistical property that measures how well a given attribute separates the training examples according to their target classification. It calculates the difference in entropy from before to after the set is split on an attribute.

3. In other words, Information Gain is the reduction in entropy. It computes the difference between entropy before split and average entropy after split of the dataset based on given attribute values.

4. Therefore, the attribute with the highest information gain is chosen as the splitting attribute for the node.