you can round them before passing them to the function for example shap.force_plot(base_value = np.around(explainer.expected_value, decimals=2), shap_values = np.around(shap_values[sample_id,:], decimals = 2), features = np.around(one_sample, decimals=2), matplotlib=True, show = ...
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通常，基于包装器的方法机器学习模型可解释性进行到底 —— 从SHAP值到预测概率（二）[译] 我见过最好...
shap.force_plot函数的源码解读 shap.force_plot(explainer.expected_value[1], shap_values[1][0,:], X_display.iloc[0,:])解读 defforce(base_value,shap_values=None,features=None,feature_names=None,out_names=None,link="identity",plot_cmap="RdBu",matplotlib=False,show=True,figsize=(20,3),ord...
示例 1: 输入: [1,3,5,6], 5 输出: 2 示例 2: 输入: [1,3,5,6], 2 输出: 1...
shap.force_plot函数的源码解读 shap.force_plot(explainer.expected_value[1], shap_values[1][0,:], X_display.iloc[0,:])解读 def force(base_value, shap_values=None, features=None, feature_names=None, out_names=None, link="identity", plot_cmap="RdBu", matplotlib=False, show=True, figsiz...
This is a fantastic package that is very useful for model explainability - thank you. A parameter in shap.force_plot() seems to behaving inconsistently with expectations - any clarifiers/pointers if this is user error is greatly apprecia...
在使用shap.force_plot函数时,你需要确保传递给函数的参数是正确的。根据你提供的代码片段shap.force_plot(explainer.expected_value[0], shap_values3[0][0,:], x_val.iloc[]),我将分点解释如何正确使用这个函数,并提供一个完整的代码示例。 1. explainer.expected_value[0] 解释:这是模型的基准值(expected...
shap.force_plot函数的源码解读 shap.force_plot(explainer.expected_value[1], shap_values[1][0,:], X_display.iloc[0,:])解读 def force(base_value, shap_values=None, features=None, feature_names=None, out_names=None, link="identity", plot_cmap="RdBu", matplotlib=False, show=True, figsiz...
Fig. 5a shows an example histogram plot of all the roughness values from the 340 images taken, with some clear extreme values present within the dataset. As seen in its corresponding box-and-whisker plot in Fig. 5b, different cut-offs were also plotted to compare whether filters based on ...