shap_interaction_values = explainer.shap_interaction_values(X) shap.summary_plot(shap_interaction_values, X) 所有特征之间的交互 对角线上是该特征自身的主要影响 print(shap_values.values[0][0])#显示第1个样本的第1个特征的shap值print(shap_values.values[0].sum())#显示第1个样本的所有特征的shap值...
How to Implement SHAP Values in Python Application of SHAP Values Conclusion Experiment with this code inRun code Share Machine learning models are becoming increasingly complex, powerful, and able to make accurate predictions. However, as these models become "black boxes," it's even harder to ...
现在可以在计算 SHAP 值时利用 GPU 硬件,从而加快整个模型解释过程。 GPUTreeShap enables massively exact calculation of the shape values for tree-based algorithms。图 4 显示了 GPUTreeSHAP 如何提供在 CPU 上使用带有 GPU 的 SHAP 时获得的增益估计。根据 GPUTreeShap: Massively Parallel Exact Calculation o...
gather(feature, shap_value) %>% mutate(feature_value = feature_values) %>% group_by(feature) %>% mutate(shap_importance = mean(abs(shap_value))) 1.3 SHAP可视化 现在,我们已经计算得到了这些特征的SHAP值,下面我们进行可视化。首先我们使用ggplot2进行可视化,严格的来说是基于ggplot2的蜂群图可视化。...
shap.summary_plot(shap_values_gpu, X_test) 图3.SHAP 汇总图 图3 中的每个点表示原始数据集中的一行。对于每个点: y 轴按重要性从上到下的顺序指示特征。 x 轴指的是实际的 SHAP 值。 点的水平位置表示特征对该特定样本的模型预测的影响。
shap.decision_plot(explainer.expected_value[1], shap_values[1][12,:], X_test.columns) Conclusion In this blog post, we have introduced SHAP values, a method for explaining the output of machine learning models. We have shown how SHAP values can be used to explain individual predictions and...
Now that we are familiar with Shapley values and their properties, we can see how they can explain a machine learning model. Suppose that we have a model f, and a feature vector x. We define the model g as: where ϕ₀ =E[f(x)], ϕᵢ are the Shapley values of f...
While SHAP values can explain the output of any machine learning model, we have developed a high-speed exact algorithm for tree ensemble methods (Tree SHAP arXiv paper). Fast C++ implementations are supported for XGBoost, LightGBM, CatBoost, scikit-learn and pyspark tree models: import xgboost ...
shap.plots.force(shap_values[0]) shap值类似回归系数:有正负之分,大小之分 如果解读这个图: shap_values[0] - 第0个样本 shap_values[0].base_values - 22.53,所有样本汇总的平均 f(x) 值,所以所有值都一样,模型在数据集上的输出均值22.53
SHAP values are used to explain individual predictions made by a model. It does this by giving the contributions of each factor to the final prediction. SHAP interaction values extend on this by breaking down the contributions into their main and interaction effects. We can use these to highlig...