Difference between Information Gain Vs Mutual Information
Criteria | Information Gain (IG) | Mutual Information (MI) |
---|---|---|
Definition | Measures reduction in uncertainty of the target variable when a feature is known. | Measures mutual dependence between two variables, indicating how much information one variable provides about the other. |
Focus | Individual feature importance | Mutual dependence and information exchange between variables |
Usage | Commonly used in decision trees for feature selection | Versatile application in feature selection, clustering, and dimensionality reduction |
Interactions | Ignores feature interactions | Considers interactions between variables, capturing complex relationships |
Applicability | Effective for discrete features with clear categories | Suitable for both continuous and discrete variables, capturing linear and nonlinear relationships |
Computation | Simple to compute | Can be computationally intensive for large datasets or high-dimensional data |
Information Gain and Mutual Information for Machine Learning
In the field of machine learning, understanding the significance of features in relation to the target variable is essential for building effective models. Information Gain and Mutual Information are two important metrics used to quantify the relevance and dependency of features on the target variable. Both information gain and mutual information play crucial roles in feature selection, dimensionality reduction, and improving the accuracy of machine learning models, and in this article, we will discuss the same.