Normalization (Data Science): 편집 역사

2024년 11월 5일 (화)

• 최신이전 09:042024년 11월 5일 (화) 09:04‎ 핵톤 토론 기여‎ 5,085 바이트 +5,085‎ Created page with "Normalization in data science is a preprocessing technique used to adjust the values of numerical features to a common scale, typically between 0 and 1 or -1 and 1. Normalization ensures that features with different ranges contribute equally to the model, improving training stability and model performance. It is especially important in machine learning algorithms that rely on distance calculations, such as k-nearest neighbors (kNN) and clustering. ==Importance of Normali..." 태그: 시각 편집