Don't Blame the Model
機械学習モデルの失敗は、多くの場合、モデル自体ではなく、データの品質や実装プロセス、評価方法など、モデルを取り巻くシステム全体の問題に起因しています。モデルを責める前に、データパイプライン、前処理、評価基準など、より広範なコンテキストを検証する必要があります。
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