我遇到了类似的问题，并最终此处.

I ran into a similar issue and ended up posting about it on /r/MachineLearning. It turns out the difference can be attributed to data standardization. Whatever approach scikit-learn is using to find the parameters of the model will yield better results if the data is standardized. scikit-learn has some documentation discussing preprocessing data (including standardization), which can be found here.

Number of 'default' values : 333
Intercept: [-6.12556565]
Coefficients: [[ 2.73145133  0.27750788]]

Confusion matrix
[[9629   38]
 [ 225  108]]

Score          0.9737
Precision      0.7397
Recall         0.3243

代码

# scikit-learn vs. R
# http://***.com/questions/28747019/comparison-of-r-statmodels-sklearn-for-a-classification-task-with-logistic-reg

import pandas as pd
import sklearn

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn import preprocessing

# Data is available here.
Default = pd.read_csv('https://d1pqsl2386xqi9.cloudfront.net/notebooks/Default.csv', index_col = 0)

Default['default'] = Default['default'].map({'No':0, 'Yes':1})
Default['student'] = Default['student'].map({'No':0, 'Yes':1})

I = Default['default'] == 0
print("Number of 'default' values : {0}".format(Default[~I]['balance'].count()))

feats = ['balance', 'income']

Default[feats] = preprocessing.scale(Default[feats])

# C = 1e6 ~ no regularization.
classifier = LogisticRegression(C = 1e6, random_state = 42) 

classifier.fit(Default[feats], Default['default'])  #fit classifier on whole base
print("Intercept: {0}".format(classifier.intercept_))
print("Coefficients: {0}".format(classifier.coef_))

y_true = Default['default']
y_pred_cls = classifier.predict_proba(Default[feats])[:,1] > 0.5

confusion = confusion_matrix(y_true, y_pred_cls)
score = float((confusion[0, 0] + confusion[1, 1])) / float((confusion[0, 0] + confusion[1, 1] + confusion[0, 1] + confusion[1, 0]))
precision = float((confusion[1, 1])) / float((confusion[1, 1] + confusion[0, 1]))
recall = float((confusion[1, 1])) / float((confusion[1, 1] + confusion[1, 0]))
print("\nConfusion matrix")
print(confusion)
print('\n{s:{c}<{n}}{num:2.4}'.format(s = 'Score', n = 15, c = '', num = score))
print('{s:{c}<{n}}{num:2.4}'.format(s = 'Precision', n = 15, c = '', num = precision))
print('{s:{c}<{n}}{num:2.4}'.format(s = 'Recall', n = 15, c = '', num = recall))