Information Gain
2024 | AI Dictionary
What is Information Gain: A measure used in decision trees to determine feature effectiveness by quantifying the reduction in uncertainty when splitting data.
What is Information Gain?
Information Gain is a measure used in decision trees to determine the effectiveness of a feature in classifying data. It quantifies the reduction in uncertainty (entropy) achieved by splitting the data based on a particular feature. A higher Information Gain indicates a more informative feature that helps in better classification .
How Information Gain Works
- Entropy: A measure of uncertainty or impurity in the data.
- Information Gain: The reduction in entropy after splitting the data on a particular feature. It is calculated as the difference between the entropy of the original set and the weighted entropy of the split subsets.
Applications of Information Gain
- Decision Trees: Helps in selecting the most important feature for splitting data at each node.
- Feature Selection : Used to identify features that provide the most information in predictive modeling tasks.
- Text Classification: Applies to text data for selecting keywords or features that are most informative for classification.
Example of Information Gain
In a decision tree for predicting whether a customer will buy a product based on age and income, if splitting the data based on age results in a significant reduction in uncertainty (information gain), age would be chosen as the first feature to split the data.
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