如何解释机器学习模型的预测？| Lil'Log
Lilian Weng 在 Lil'Log 撰文梳理机器学习模型的可解释性方法，从线性回归、朴素贝叶斯、决策列表到随机森林等本身可解释的模型入手。文章引用 Lipton (2017) 提出的可模拟性、算法透明性与可分解性三项标准，并以金融放贷、医疗诊断、司法再犯预测和自动驾驶等场景说明解释模型决策的必要性。
the corresponding interpretable data representation $x b$, let us sample instances around $x b$ by drawing nonzero elements of $x b$ uniformly at random where the number of such draws is also uniformly sampled. This process generates a perturbed sample $z b$ which contains a fraction of nonzero elements of $x b$. Then we recover $z b$ back into the original input $z$ and get a prediction score $f(z)$ by the target model. Use many such sampled data points $z b \in \mathcal{Z} b$ and their model p
匹配字段正文