Feature importance is only defined when the decision tree model is chosen as base learner (`booster=gbtree`). It is not defined for other base learner types, such as linear learners .仅当选择决策树模型作为基础学习者(`booster=gbtree`)时,才定义特征重要性。它不适用于其他基本学习者类型,例如线...
Feature importance in sklearn interface used to normalize to 1,it's deprecated after 2.0.4 and is the same as Booster.feature_importance() now. ``importance_type`` attribute is passed to the function to configure the type of importance values to be extracted. """ if self._n_features is...
与一般模型直接使用 Shap 有所不同,使用 model.get_feature_importance() 方法,并设置参数 type='ShapValues', 直接输出 shap_values 值,该值可直接用户输出结果值及绘制相应可视化图形。 shap_values = model.get_feature_importance( pool1, type='ShapValues') expected_value = shap_values[0,-1] shap_va...
Feature importance in sklearn interface used to normalize to 1,it's deprecated after 2.0.4 and is the same as Booster.feature_importance() now. ``importance_type`` attribute is passed to the function to configure the type of importance values to be extracted. """ if self._n_features is...
model = CatBoostClassifier(iterations=1000, learning_rate=0.03, loss_function="MultiClass") # 训练模型 model.fit(train_pool) # 输出特征重要性 feature_importance = model.get_feature_importance() print("Feature importance: ", feature_importance) ...
LGBMClassifier LGBMClassifier.feature_importances_函数,采用split方式计算 LGBMC.feature_importances_ importance_type='split', def feature_importances_(self): """Get feature importances. Note --- Feature importance in sklearn interface used to normalize to 1,it's deprecated after 2.0.4 and is ...
transfer_model=CatBoostClassifier(learning_rate=0.1,depth=6,iterations=100,model_shrink_mode='feature_importance')# 进行迁移学习 transfer_model.fit(X_finetune,y_finetune) 结果评估 最后,我们可以使用深度集成或迁移学习后的模型对测试集进行预测,并评估模型的性能。以下是一个简单的示例: ...
指数损失函数(Exponential loss),与AdaBoostClassifier的损失函数一致,相对对数损失来说对错误标签的样本不够鲁棒,只能够被用来作二分类 常用方法 特征重要性(feature_importances_):进行特征重要性的评估 包外估计(oob_improvement_),使用包外样本来计算每一轮训练后模型的表现提升 ...
y = data['target']# 定义Pool对象pool = Pool(X, label=y)# 定义模型model = CatBoostClassifier()# 训练模型model.fit(pool)# 获取特征重要性feature_importance = model.get_feature_importance(pool)# 打印特征重要性print("Feature Importance:", feature_importance) ...
model=CatBoostClassifier()# 训练模型 model.fit(pool)# 获取特征重要性 feature_importance=model.get_feature_importance(pool)# 打印特征重要性print("Feature Importance:",feature_importance) SHAP值 SHAP(SHapley Additive exPlanations)是一种解释机器学习模型预测的方法,可以提供每个特征对模型预测的贡献度。以下是...