Final answer:
Information Gain is used in decision trees in machine learning to determine the best attribute for splitting a dataset. It's calculated by subtracting the weighted average entropy of the children from the entropy of the parent. The attribute with the highest Information Gain is chosen for the split.
Step-by-step explanation:
The formula you've mentioned, Information Gain(parent, children), is used within the field of decision trees in machine learning, a subset of artificial intelligence in Computers and Technology. Information Gain is a measure that is used to select the splitting attribute that partitions data into the best possible subsets to make clear distinctions between different classes. This concept is based on entropy, which represents the amount of uncertainty or impurity in the dataset.
To calculate Information Gain, you subtract the weighted average entropy of the child nodes (subsets after the split) from the entropy of the parent node (the original dataset before the split). The attribute that results in the highest Information Gain is usually chosen for splitting the dataset at that particular node in the tree. The formula is given by:
InformationGain = Entropy(parent) - Sum((weight of child) * Entropy(child))
where Entropy is a measure of the randomness in the information being processed, and the weight of child is the proportion of the number of elements in the child group compared to the number of elements in the parent.