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 = False, feature_names=feat_names) 👎 5 Author dynamik1703 commente...
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...
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CatBoost's predictions are great, but the SHAP values are way off.. I'm sensing it has to do sg with the fact that there was no categorical data preprocessing, and due to some inner-model captured interactions the clear effects cannot be calcualted by shap. Let me know if you also ne...
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...
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...
在这里，我们将研究SHAP值，这是一种解释来自机器学习模型的预测的有效方法。
Hi, I tried to save the dependency plots to pdf after adding parameter show = false. It worked. Now I need to save the output of shap.force_plot into pdf. It doesn't allow to add show = false parameter. If I try to save the plot without ...
为了达到这两个效果，区块链的共识、同步、校验等技术细节足可大书特书，而本文要从“我篡改了区块链...