在XGBoost使用的内部数据结构中定义数据矩阵时,当我提到特征名称时,我得到了这个错误: 代码语言:javascript 复制 d_train=xgboost.DMatrix(X_train,label=y_train,feature_names=list(X))d_test=xgboost.DMatrix(X_test,label=y_test,feature_names=list(X))...shap_values=shap.TreeExplainer(model).shap_values...
: ValueError: feature_names mismatch: ['f0', 'f1', 'f2', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'f10', 'f11', 'f12', 'f13', 'f14', 'f15', 'f16', 'f17', 'f18', 'f19', 'f20', 'f21', 'f22', 'f23', 'f24', 'f25', 'f26', 'f27', 'f28', ...
xgboost\core.py", line 869, in feature_names raise ValueError('feature_names may not contain [, ] or <') ValueError: feature_names may not contain [, ] or < 解决思路 错误地址:xgboost\core.py", line 869, 值错误:feature_names中不能包含[,]或<的符号 解决方法 '添加以下三行解决问题' im...
我尝试使用新数据使用我的xgboost酸菜模型获得预测结果,但得到错误消息"ValueError: Feature shape mismatch“ 这样做的原因是,我需要完全按照构建模型的顺序传递功能名称feature_names Y_static = model_pkl.predict(X_new) feature_order返回"None“,它在xgboost但现在我使用的是xgboost版 浏览30提问于2021-09-15得票...
= feature_names: dat_missing = set(cast(FeatureNames, self.feature_names)) - set( cast(FeatureNames, feature_names) ) my_missing = set(cast(FeatureNames, feature_names)) - set( cast(FeatureNames, self.feature_names) ) msg = "feature_names mismatch: {0} {1}" if dat_missin...
ValueError: feature_names mismatch:["f1","f2","f3","f4","f5","f6", ……"f60123", ] 原因分析 - 上网百度,大多回答都是: 训练集和测试集的列名不一致 训练集和测试集的列名顺序不一致 如果有以上两种的可以尝试进行修改 - 笔者遇到的是另外一种: ...
The role of the SHAP summary plot is to rank the feature contribution of the warning model from a global perspective. In Fig. 5, it shows the names of the features on the left, and the right color is from blue to purple to red, indicating the value of the feature from low to high...
在使用 xgboost 训练数据和检验模型时,如果测试集的特征最大编号数大于训练集的特征最大编号数,在测试时,会报 ValueError: feature_names mismatch 和 training data did not have the following fields 错误。 1具体报错信息 2解决问题方法 2.1libsvm 样本都追加最大特征编号 具体报错信息 Traceback (most recent ...
1691 raise ValueError(msg.format(self.feature_names, -> 1692 data.feature_names)) 1693 1694 def get_split_value_histogram(self, feature, fmap=”, bins=None, as_pandas=True): ValueError: feature_names mismatch: [‘f0’, ‘f1’, ‘f2’, ‘f3’, ‘f4’, ‘f5’, ‘f6’, ‘f7’, ...
feature_names, self.output_name, mode="classifier" ) if _is_macos() and _macos_version() >= (10, 13): # Get predictions df = pd.DataFrame(self.X, columns=self.feature_names) probabilities = xgb_model.predict_proba(self.X) df["classProbability"] = [ dict(zip(xgb_model.classes_,...