Information Gain 편집하기 (부분)

==Applications of Information Gain==
Information gain is used in various machine learning tasks and model types:
*'''Classification Trees''': Information gain helps create splits that separate classes effectively, commonly used in decision trees and random forests.
*'''Feature Selection''': Information gain ranks features based on their predictive power, aiding in selecting the most informative features for models.
*'''Text Classification''': In natural language processing, information gain can rank words or phrases based on their importance in distinguishing between classes, such as spam vs. not spam in email filtering.
*'''Image Processing''': Used to identify critical features or regions in images that aid in classifying objects or patterns.