Random Forest: 편집 역사

2024년 11월 4일 (월)

• 최신이전 11:392024년 11월 4일 (월) 11:39‎ 핵톤 토론 기여‎ 3,898 바이트 +3,898‎ Created page with "'''Random Forest''' is an ensemble learning method that combines multiple Decision Trees to improve classification or regression accuracy. It is designed to mitigate the limitations of single Decision Trees, such as overfitting and sensitivity to data variations, by building a "forest" of trees and aggregating their predictions. This approach often leads to greater model stability and accuracy. ==How It Works== Random Forest creates multiple Decision Trees during trainin..." 태그: 시각 편집