What Is Information Gain? A Core Metric in Machine Learning Explained
In machine learning, Information Gain (IG) quantifies how much 'information' a feature gives us about the class labels. It’s rooted in entropy—a concept from information theory that measures disorder. When building decision trees, algorithms use IG to decide which feature to split the data on at each step. The higher the Information Gain, the more effectively a feature separates data into target classes. It’s computed as the reduction in entropy after the dataset is split. This makes IG central to decision tree algorithms like ID3, C4.5, and CART, and valuable for any model that benefits from strong feature selection.
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Use Cases
Information Gain decides which features decision trees prioritize—accelerating training and boosting predictive accuracy.
Ranking features based on Information Gain filters out noise, enhancing model performance across classifiers like Naive Bayes or Random Forests.
High Information Gain helps models focus on meaningful splits, minimizing overfitting on irrelevant or correlated variables.
Improving Data Interpretability
Frequently Asked Questions
How is Information Gain calculated?
Information Gain = Entropy(before split) - Weighted Entropy(after split). The entropy is calculated using the probability distribution of class labels in your dataset.
Is Information Gain the same as Gini Index?
No. Both are splitting criteria, but Information Gain uses entropy; the Gini Index measures impurity in a different way. Gini is typically faster but IG often yields deeper insights.
Why does Information Gain matter in decision trees?
Because it selects the feature that reduces uncertainty the most, leading to purer decision nodes and better predictive performance.
Can Information Gain handle continuous variables?
Partially. Many implementations handle missing data heuristically—either ignoring or weighting splits. Advanced methods like Gain Ratio can compensate better.
Does Information Gain work with missing data?
You calculate the Information Gain for each feature and keep the top-performing ones. This reduces dimensionality and improves algorithm efficiency.
Related Terms
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