SHAP Analysis: 편집 역사

2024년 11월 30일 (토)

• 최신이전 07:382024년 11월 30일 (토) 07:38‎ Fortify 토론 기여‎ 2,985 바이트 +2,985‎ Created page with "'''SHAP Analysis''' (SHapley Additive exPlanations) is a machine learning interpretability technique based on cooperative game theory. It is used to explain the predictions of complex machine learning models by attributing the contribution of each feature to the model's output. SHAP values provide a consistent and mathematically sound way to interpret individual predictions and global feature importance. ==Overview== SHAP values are derived from Shapley values, a concept..." 태그: 시각 편집