Q19Machine Learning
Question
Discuss Decision Trees in detail. Explain the concepts of Entropy, Information Gain, and the ID3 algorithm.
Answer
Decision Trees split data based on feature values to maximize information gain and minimize entropy.
### Decision Tree Overview A decision tree is a flowchart-like structure where each internal node represents a feature, each branch represents a decision rule, and each leaf node represents an outcome.
### Entropy and Information Gain - Entropy: A measure of impurity or randomness in the data. - Information Gain: The reduction in entropy after splitting the dataset on an attribute. The attribute with the highest gain is chosen as the splitting node.
### ID3 Algorithm The ID3 (Iterative Dichotomiser 3) algorithm builds trees top-down: 1. Calculate the entropy of the current dataset. 2. For each attribute, calculate the Information Gain. 3. Select the attribute with the highest Information Gain as the decision node. 4. Split the dataset and recursively apply the process to the child nodes until all instances belong to the same class.
