Information Gain 편집하기
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핵톤 (토론 | 기여)님의 2024년 11월 5일 (화) 06:06 판 (Created page with "Information Gain is a metric used in machine learning to measure the effectiveness of a feature in classifying data. It quantifies the reduction in entropy (impurity) achieved by splitting a dataset based on a particular feature. Information gain is widely used in decision tree algorithms to select the best feature for each node split, maximizing the model’s predictive accuracy. ==Definition of Information Gain== Information gain is defined as the difference in entropy...")