Last Updated:2025/12/04

In many real-world problems, random forests outperform single decision tree models because they reduce variance by averaging multiple trees.

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In many real-world problems, random forests outperform single decision tree models because they reduce variance by averaging multiple trees.

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多くの実世界の問題では、複数のランダムフォレストモデルが単一の決定木モデルよりも優れており、複数の木を平均化することで分散を低減します。

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