long_df = pd.DataFrame([values,labels]).T ; long_df.columns = ['Values', 'Features'] title = 'Range of SHAP values per sample across all\ncross-validation repeats' xlab, ylab = 'SHAP Value Variability', 'SHAP range per sample' sns.catplot(data = long_df, x = 'Features', y = ...
random_state=10)model=RandomForestRegressor(random_state=10)# Random stateforreproducibility(same results every time)fit=model.fit(X_train,y_train)yhat=fit.predict(X_test)result=mean_squared_error(y_test,yhat)print('RMSE:',round(np.sqrt(result),4))# UseSHAPto explain predictions...
Explain using SHAP import shap shap.initjs() # Convert the data into a dataframe with column names for easy plotting X_test = pd.DataFrame(data=x_test,columns=reduced_data.columns[3:]) # Instantiate the SHAP Kernel explainer = shap.KernelExplainer(model.predict, shap.sample(x_train[::50...
相同的语法适用于LightGBM、CatBoost和scikit-learn模型explainer=shap.TreeExplainer(xgb)shap_values=explainer.shap_values(X_test)shap_values###shap_values1=np.array(shap_values).reshape(23,36)X_shap=pd.DataFrame(shap_values1)X_shap.head()print('Expected Value: ',explainer....
( handle_unknown='ignore', sparse=False))forfincategorical] transformations = numeric_transformations + categorical_transformations# append model to preprocessing pipeline.# now we have a full prediction pipeline.clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)), ('classifier',...
• 使用Alibi Explain [^2] 包应用 ALE 。 我们将看到,与其他 XAI 方法(如 SHAP ([[Python 中的 SHAP 简介]])、LIME ([[深入研究 LIME 的本地解释]])、ICE 图([[PDP 和 ICE 图的终极指南]]) 和 Friedman 的 H-stat)不同,ALE 给出的解释对多重共线性具有稳健性。
dask, dask-ml - Pandas DataFrame for big data and machine learning library, resources, talk1, talk2, notebooks, videos. h2o - Helpful H2OFrame class for out-of-memory dataframes. cuDF - GPU DataFrame Library, Intro. cupy - NumPy-like API accelerated with CUDA. ray - Flexible, high-perfo...
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onnxruntime as ort@st.cache_resourcedefload_onnx_model():return ort.InferenceSession("model.onnx")异常处理try: prediction = model.predict(data)except Exception as e: st.error(f"预测失败: {str(e)}")结果解释import shap@st.cache_resourcedefexplain_model(): explainer = shap.TreeExp...
如何将SHAP本地解释导出到dataframe? 如果你有这样的模型: import xgboostimport shapimport warningswarnings.filterwarnings("ignore")# train XGBoost modelX,y = shap.datasets.boston()model = xgboost.XGBRegressor().fit(X, y)# explain the model's predictions using SHAP values# (same syntax works for ...