3.1 SHAP 与Shapley Value SHAP方法是Shapley Additive exPlanations的缩写。按照时间的先后顺序,Shapley Value先提出(1953年),SHAP后提出(2017年)。这一点,从名字上可见一斑。 Shapley 来自于合作博弈论的方法,主要用来解决在多人合作的模式下,如何公平得分配收益的问题。 下面是一个非常简单生动的例子,举例了Shapley...
The Shapley value is the average contribution of a feature value to the prediction in different coalitions. The Shapley value is NOT the difference in prediction when we would remove the feature from the model. Shapley value是针对feature value的而不是feature的(x1是该instance对应的x1的值,否则是平...
SHAP值(Shapley additive explanation)是一种广泛使用的解释模型的方法。虽然SHAP值和SHAPley Value听起来很像,但它们其实是不同的概念。SHAP值是SHAPley Value的近似算法,可以提供全局和局部的特征贡献解释。🌰 局部解释:在单个观察值的预测中,SHAP值图可以告诉我们这个观察值的预测值Y_pred与所有Y_pred的平均值E(Y...
SHAP(Shapley Additive exPlanations)使用来自博弈论及其相关扩展的经典 Shapley value将最佳信用分配与局部解释联系起来,是一种基于游戏理论上最优的 Shapley value来解释个体预测的方法。 从博弈论的角度,把数据集中的每一个特征变量当成一个玩家,用该数据集去训练模型得到预测的结果,可以看成众多玩家合作完成一个项目...
value, and using Shapley equations to linearize components such as max, softmax, products, divisions, etc. Note that some of these enhancements have also been since integrated into DeepLIFT. TensorFlow models and Keras models using the TensorFlow backend are supported (there is also preliminary ...
value, and using Shapley equations to linearize components such as max, softmax, products, divisions, etc. Note that some of these enhancements have also been since integrated into DeepLIFT. TensorFlow models and Keras models using the TensorFlow backend are supported (there is also preliminary ...
We present SHAPNN, a novel deep tabular data modeling architecture designed for supervised learning. Our approach leverages Shapley values, a well-established technique for explaining black-box models. Our neural network is trained using standard backward propagation optimization methods, and is ...
shapley_value(train[col_list].head(1),model,max_display=10) 1. 瀑布图中给出了基准线E[f(x)] = -1.445,对应的预测概率计算方式为取反logit:p=1-1/(math.exp(-1.445)+1)=0.19为平均预测概率,此处interestRate贡献度为0.74。瀑布图更加直观地展示出模型对于单个样本预测结果的解释,该样本的预测结果0.3...
SHAP值基于博弈论中的 Shapley 值。在博弈论中,Shapley值有助于确定协作博弈中的每个玩家对总支出的贡献。 对于机器学习模型,每个特征都被视为一个“玩家”。要素的 Shapley 值表示该要素在所有可能的特征组合中的贡献的平均量级。 具体而言,SHAP 值是通过比较存在和不存在特定特征的模型预测来计算的。这是针对数据...
Unlike previous gradient-based approaches, Shap-CAM gets rid of the dependence on gradients by obtaining the importance of each pixel through Shapley value. We demonstrate that Shap-CAM achieves better visual performance and fairness for interpreting the decision making process. Our approach outperforms...