而shap value不仅可以解释这些问题,还具有局部的解释性,即反映出每一个样本中的特征的影响力,并同时表现出正负性。 使用Feature Importance查看特征重要性 model.fit(x_train,y_train)importances=model.feature_importances_indices=np.argsort(importances)[::-1]forfin
在这些名字当中除了我们很熟悉的 Feature Importance,还有一个被目前最广泛应用的方法 Shap,也就是我们今天的主角。 二、feature Importance VS. shap值 特征重要性 (Feature Importance)可以帮助我们在成百上千的特征池中,找到影响最大的特征,极大得增强了模型的可解释性,也是做特征筛选的重要参考指标。 但是在实际...
使用模型预测,得到样本预测的:pred_cat 使用模型预测全样本的shap值:cat.get_feature_importance(data = Pool(X_all, cat_features=cat_features), type = 'ShapValues') 用一元插值函数拟合f(shap_sum,pred_cat),其中shap_num代表每个样本shap值加总 利用上面函数拟合f(shap_sum - 特征值),获得新的概率值,...
In particular, we demonstrate a common thread among the out-of-bag based bias correction methods and their connection to local explanation for trees. In addition, we point out a bias caused by the inclusion of inbag data in the newly developed SHAP values and suggest a remedy....
shap_values=explainer(X_test)returnexplainer, shap_values#4. 特征重要性排序图defplot_feature_importance(shap_values, X_train): plt.figure(figsize=(10, 6)) shap.summary_plot(shap_values.values, X_train, plot_type="bar", show=False) ...
XGBoost builds models by incrementally adding decision trees, each addressing the errors of the previous one, which can result in inflated feature importance scores due to the method's emphasis on misclassified examples. While SHAP values provide a theoretically robust way to interpret predictions, ...
#Plot shap decision treeexpected_values = explainer.expected_valueshap_array = explainer.shap_values(X_test2)shap.decision_plot(expected_values, shap_array[0:10],feature_names=list(X.columns))SHAPs的瀑布图显示了单个预测，以及它们如何受到每个特征及其得分的影响。这个瀑布图显示了当每个特征得分被应用...
shap.decision_plot(expected_values, shap_array[0:10],feature_names=list(X.columns)) SHAPs的瀑布图显示了单个预测,以及它们如何受到每个特征及其得分的影响。这个瀑布图显示了当每个特征得分被应用时,它们是如何在每个方向上偏离的。这使我们能够看到每个特征对预测的影响。
# compute the global importance of each feature as the mean absolute value # of the feature's importance over all the samples global_importances = np.abs(shap_values).mean(0)[:-1] 1. 2. 3. [output]: global_importances array([[3.70270513e-04, 1.11664905e-02, 8.02847521e-02, ...,...
– 使用模型预测,得到样本预测的:pred_cat – 使用模型预测全样本的shap值:cat.get_feature_importance(data = Pool(X_all, cat_features=cat_features), type = 'ShapValues') – 用一元插值函数拟合f(shap_sum,pred_cat),其中shap_num代表每个样本shap值加总 – 利用上面函数拟合f(shap_sum - 特征值),...