也可以参考这篇博客[One Feature Attribution Method to (Supposedly) Rule Them All: Shapley Values](https://towardsdatascience.com/one-feature-attribution-method-to-supposedly-rule-them-all-shapley-values-f3e04534983d),
Our results supported this hypothesis as we found higher Shapley values for marker genes compared with randomly chosen features in the Patch-seq GABAergic neuron dataset. Furthermore, we used the Shapley values as a predictor for marker genes. Our results showed that the marker features had ...
SHapley Additive exPlanation (SHAP) values were calculated to determine the prediction role of each feature in the model with the highest predictive performance. Results This analysis included 10,064 participants, with 353 identified as having comorbid CVD and cancer. After excluding collinear features,...
Patients who reached the endpoint obtained the lowest values for partial pressure of oxygen, platelets, lymphocytes, monocytes and eosinophils and the highest values for CRP, age, procalcitonin, urea, LDH, RDW, creatine kinase, creatinine, D-dimer and glucose. In the case of baseline treatments,...
The code itself hasn't necessarily been optimized for speed. The speed advantage ofshapFlexcomes in the form of giving the user the ability to select 1 or more target features of interest and avoid having to compute Shapley values for all model features. This is especially useful in high-dim...
SHAP vision SHAP (SHapley Additive exPlanations) is a popular explanation method for deep neural networks that provides insights into the contribution of each input feature to a given prediction. It's based on the concept of Shapley values, which is a method for assigning credit to individual pla...
shap.force_plot(k_explainer.expected_value[1], k_shap_values[1], data_for_prediction) 运行结果的图表类似如下图形, 其中左边(红色)代表当前样本相对于baseline增加的预测值 右边(蓝色)代表当前样本相对于baseline减少的预测值 左边(红色) - 右边(蓝色) => output_value - base_value ...
SHAP textSHAP (SHapley Additive exPlanations) is a popular explanation method for deep neural networks that provides insights into the contribution of each input feature to a given prediction. It's based on the concept of Shapley values, which is a method for assigning credit to individual players...
BorutaShap: BorutaShap is a Python library for feature selection that combines two powerful techniques: Boruta algorithm and Shapley values method EvalML: "EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions." -- Its...
Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artificial intellig