我有以下非常简单的代码，尝试对一个简单的数据集进行建模：
from sklearn.pipeline import Pipelinefrom sklearn.impute import SimpleImputerfrom sklearn.preprocessing import StandardScalerfrom sklearn.linear_model import LinearRegressionfrom sklearn.model_selection import GridSearchCVdata = {'Feature_A': [1, 2, 3, 4], 'Feature_B': [7, 8, 9, 10], 'Feature_C': [2, 3, 4, 5], 'Label': [7, 7, 8, 9]}data = pd.DataFrame(data)data_labels = data['Label']data = data.drop(columns=['Label'])pipeline = Pipeline([('imputer', SimpleImputer()), ('std_scaler', StandardScaler())])data_prepared = pipeline.fit_transform(data)lin_reg = LinearRegression()lin_grid = {"n_jobs": [20, 50]}error = "max_error"grid_search = GridSearchCV(lin_reg, param_grid=lin_grid, verbose=3, cv=2, refit=True, scoring=error, return_train_score=True)grid_search.fit(data_prepared, data_labels)print(grid_search.best_estimator_.coef_)print(grid_search.best_estimator_.intercept_)print(list(data_labels))print(list(grid_search.best_estimator_.predict(data_prepared)))这给我带来了以下结果：
[0.2608746 0.2608746 0.2608746]7.75[7, 7, 8, 9][6.7, 7.4, 8.1, 8.799999999999999]从这里看，是否有办法计算出能够在数据集的边界内给我带来最大标签值的特征值？
回答：
如果我正确理解了你的问题，以下方法应该可行：
import numpy as npid_max = np.argmax(grid_search.predict(data)) # 查找预测标签最大值的IDprint(data.loc[id_max])