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...
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....
特征互动效应特征互动效应是指两个或多个特征之间的相互作用对模型预测结果的影响。在SHAP库中，可以使用...
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 7). lime_xgboost - Create LIMEs for XGBoost. eli5 - Inspecting machine learn...
from sklearn.pipeline import Pipeline from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.linear_model import LogisticRegression from sklearn_pandas import DataFrameMapper # assume that we have created two arrays, numerical and categorical,...
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LightGBM supports weighted training. It uses an additional file to store weight data, like the following: :: 1.0 0.5 0.8 ... It means the weight of the first data row is ``1.0``, second is ``0.5``, and so on. The weight file corresponds with data file line by line, and has per...
Modin's DataFrame features the same API as the Pandas' equivalent, but it can leverage external frameworks for distributed data processing in the background, such as Ray [28] or Dask. Benchmarks by the developers show that data can be processed up to four times faster on a laptop with ...
在Python中进行SHAP分析,可以帮助我们理解机器学习模型的预测结果,特别是每个特征对模型预测的贡献度。以下是对SHAP分析的一个详细解答,涵盖了基本概念、安装与导入、基本使用方法、图表解读以及高级功能。 1. SHAP库的基本概念和用途 SHAP(SHapley Additive exPlanations)是一种基于合作博弈论的特征重要性解析方法。它通过...
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 Let’s ...