shap_interaction_values = explainer.shap_interaction_values(X) shap.summary_plot(shap_interaction_values, X) dependence_plot 为了理解单个feature如何影响模型的输出,我们可以将该feature的SHAP值与数据集中所有样本的feature值进行比较。由于SHAP值表示一个feature对模型输出中的变动量的贡献,下面的图表示随着特征RM...
force_plot1 = shap.force_plot(explainer.expected_value, np.mean(shap_values, axis=0), np.mean(X_test, axis=0),feature_label,matplotlib=True, show=False) shap_interaction_values = explainer.shap_interaction_values(X_test) shap.summary_plot(shap_interaction_values,X_test) 结果如图5-2所示:...
df.describe() # Calculate degradation feature df['degradation_feature'] = df['Re'] * df['Rct'] # Define the maximum expected RUL max_expected_rul = 1000 # Calculate RUL df['RUL'] = max_expected_rul / (df['degradation_feature'] + 1) df df.describe() from sklearn.model_selection ...
AI代码解释 # 加载合成数据np.array(['treaet_A'ifx==1else'cotol'forxintrtent])# 处理/控制名称RnFostRgesor()# 为model_tau_feature指定模#在基础学习器中使用feature_importances_方法plot_ipornce()#绘制shap值pot_shp_ues()# interaction_idx设置为'auto'ploshp_dpedece() 01 02 03 04 提升树...
y = df.iloc[:,:-1],df['target']X_train,X_test,y_train,y_test = train_test_split(X,y...
# 加载合成数据np.array(['treaet_A' if x==1 else 'cotol' for x in trtent]) # 处理/控制名称RnFostRgesor() # 为model_tau_feature指定模# 在基础学习器中使用feature_importances_方法plot_ipornce()# 绘制shap值pot_shp_ues()# interaction_idx设置为'auto'ploshp_dpedece() ...
# 加载合成数据 np.array(\['treaet_A' if x==1 else 'cotol' for x in trtent\]) # 处理/控制名称 RnFostRgesor() # 为model\_tau\_feature指定模 # 在基础学习器中使用feature\_importances\_方法 plot_ipornce() # 绘制shap值 pot\_shp\_ues() # interaction_idx设置为'auto' ploshp_...
# 加载合成数据np.array(['treaet_A' if x==1else 'cotol' for x in trtent]) # 处理/控制名称RnFostRgesor() # 为model_tau_feature指定模# 在基础学习器中使用feature_importances_方法plot_ipornce()# 绘制shap值pot_shp_ues()# interaction_idx设置为'auto'ploshp_dpedece() ...
shap - Explain predictions of machine learning models, talk, Good Shap intro. shapiq - Shapley interaction quantification. treeinterpreter - Interpreting scikit-learn's decision tree and random forest predictions. lime - Explaining the predictions of any machine learning classifier, talk, Warning (Myth...
shap.plots.scatter(s, color=shap_val, ymin=-0.5, ymax=1) Here, we would come to the conclusions: car_weight and year might be dropped, depending on the specify aim of the model. Add a regression spline for driver_age. Add an interaction between car_power and town. Build strong GLM...