I use a same dataset to train logistic regression model both in R and python sklearn. The dataset is unbalanced. And I find that the auc is quite different. This is the code of python:
model_logistic = linear_model.LogisticRegression() #auc 0.623
model_logistic.fit(train_x, train_y)
pred_logistic = model_logistic.predict(test_x) #mean:0.0235 var:0.023
print "logistic auc: ", sklearn.metrics.roc_auc_score(test_y,pred_logistic)
This is the code of R:
glm_fit <- glm(label ~ watch_cnt_7 + bid_cnt_7 + vi_cnt_itm_1 +
ITEM_PRICE + add_to_cart_cnt_7 + offer_cnt_7 +
dwell_dlta_4to2 +
vi_cnt_itm_2 + asq_cnt_7 + watch_cnt_14to7 + dwell_dlta_6to4 +
auct_type + vi_cnt_itm_3 + vi_cnt_itm_7 + vi_dlta_4to2 +
vi_cnt_itm_4 + vi_dlta_6to4 + tenure + sum_SRCH_item_7 +
vi_cnt_itm_6 + dwell_itm_3 +
offer_cnt_14to7 + #
dwell_itm_2 + dwell_itm_6 + CNDTN_ROLLUP_ID +
dwell_itm_5 + dwell_itm_4 + dwell_itm_1+
bid_cnt_14to7 + item_prchsd_cnt_14to7 + #
dwell_itm_7 + median_day_rate + vb_ratio
, data = train, family=binomial())
p_lm<-predict(glm_fit, test[1:nc-1],type = "response" )
pred_lm <- prediction(p_lm,test$label)
auc <- performance(pred_lm,'auc')@y.values
The auc of python is 0.623 while the R is 0.887. So I want to know what's wrong with sklearn logistic regression and how to fix it. Thanks.
In the python script, you should use predict_proba to get the probability estimates for both classes and take the second column for the positive class as the input for roc_auc_score because ROC curve is plotted by varying the probability thresholds.
pred_logistic = model_logistic.predict_proba(test_x)[:,1]
来源：https://stackoverflow.com/questions/37872536/why-the-auc-is-so-different-from-logistic-regression-of-sklearn-and-r