使用 gridsearchcv 进行逻辑回归的特征重要性

Question

I've trained a logistic regression model like this:

reg = LogisticRegression(random_state = 40)
cvreg = GridSearchCV(reg, param_grid={'C':[0.05,0.1,0.5],
                                      'penalty':['none','l1','l2'],
                                      'solver':['saga']},
                     cv = 5)
cvreg.fit(X_train, y_train)

Now to show the feature's importance I've tried this code, but I don't get the names of the coefficients in the plot:

from matplotlib import pyplot

importance = cvreg.best_estimator_.coef_[0]
pyplot.bar([x for x in range(len(importance))], importance)
pyplot.show()

Obviously, the plot isn't very informative. How do I add the names of the coefficients to the x-axis?

The importance of the coeff is:

cvreg.best_estimator_.coef_
array([[1.10303023e+00, 7.48816905e-01, 4.27705027e-04, 6.01404570e-01]])

Answer 1

The coefficients correspond to the columns of X_train , so pass in the X_train names instead of range(len(importance)) .

Assuming X_train is a pandas dataframe:

import matplotlib.pyplot as plt

features = X_train.columns
importance = cvreg.best_estimator_.coef_[0]

plt.bar(features, importance)
plt.show()

Note that if X_train is just a numpy array without column names, you will have to define the features list based on your own data dictionary.