Python 3.x 获取Scikit学习估计器最大值的特征

我有以下非常简单的代码试图对一个简单的数据集建模：

from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import GridSearchCV

data = {'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)))

这给了我以下结果：

[0.2608746 0.2608746 0.2608746]
7.75
[7, 7, 8, 9]
[6.7, 7.4, 8.1, 8.799999999999999]

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在此基础上，是否有一种方法可以计算出数据集边界内的特征值，从而为我提供最大标签？

如果我正确理解了您的问题，这应该可以：

import numpy as np
id_max = np.argmax(grid_search.predict(data)) # find id of the maximum predicted label
print(data.loc[id_max])