The correct answer is: C. b, c and d
Information gain is a measure of how much information an attribute provides about the class of a data point. It is used in decision trees to select the best attribute to split on at each node.
ID3 is a decision tree algorithm that uses information gain to select the best attribute to split on at each node. It is a greedy algorithm, which means that it always chooses the attribute that provides the most information gain at each step, without considering the consequences of its choices later in the tree.
Information gain is not biased towards single-valued or multi-valued attributes. It can be used with any type of attribute.
The approach used by ID3 is greedy. This means that it always chooses the attribute that provides the most information gain at each step, without considering the consequences of its choices later in the tree. This can lead to suboptimal trees, but it is a simple and effective algorithm.
Here is a more detailed explanation of each option:
- a. It is biased towards single-valued attributes
This is not true. Information gain can be used with any type of attribute, including single-valued and multi-valued attributes.
- b. It is biased towards multi-valued attributes
This is also not true. Information gain can be used with any type of attribute, including single-valued and multi-valued attributes.
- c. ID3 makes use of information gain
This is true. ID3 is a decision tree algorithm that uses information gain to select the best attribute to split on at each node.
- d. The approach used by ID3 is greedy
This is also true. The approach used by ID3 is greedy. This means that it always chooses the attribute that provides the most information gain at each step, without considering the consequences of its choices later in the tree.