I've tested out various feature selection methods, such as the F-test, Mutual Information and the Extra Tree (Extra Randomised) Forest Classifier (ETC) as well as PCA (which is technically a feature extraction method), with ETC being used solely for feature selection rather than as a classifier, and have subsequently performed 10-fold cross validation of my models with a combination of GridSearch and pipeline from the wonderful scikit-learn Python package, with these models being random forests, SVM, KNN and logistic regression.

Upon doing this, I found that the average AUC score for the validation sets was the highest for the models when using Extra Tree Classifier as the feature selector, with SVM performing particularly well, and the rest okay, except for logistic regression, which blatantly underperformed compared to the other models, with an average AUC for logistic regression and ETC combined of 0.4761. Weirdly enough, the best performing permutation that was captured upon doing GridSearchCV involved using $L1$ penalty, $C = 0.1$, and selected features of $n = 20$. Since thousands of permutations were performed for each model, this means that some validation AUC scores were in the range of 0.4 and 0.3, which is very unusual given that most sources online state that $0.5\leq AUC \leq 1$, which makes sense.

However, other sources do state than when $AUC<0.5$ this is due to a classification error made by the machine when executing the algorithm, and that one straightforward way to overcome this issue is by doing $1-AUC,$ whilst others state that $AUC<0.5$ indicates that the classifier model is worse than one which classifies completely at random, and this is where my confusion arises. So far I have taken the heuristic approach of subtracting the AUC from 1, but I'm very skeptical of doing this since it might be too heuristic for it to be effective. My current code for logistic regression looks as follows

def logistic(data, outcome):

X_test, y_test = data, outcome

pipe = Pipeline([('a', RFE(ExtraTreesClassifier(n_estimators=400),20,step=1000)),('b',LogisticRegression(C=100))])
pipe.fit(X_train, y_train)
auc_score = roc_auc_score(y_test, pipe.predict_proba(X_test)[:,1]))

if auc_score < 0.5:
    fpr_svc, tpr_svc, _ = roc_curve(y_test, pipe.predict_proba(X_test)[:,1], pos_label=0)
    auc_score = 1 - auc_score
    fpr_svc, tpr_svc, _ = roc_curve(y_test, pipe.predict_proba(X_test)[:,1])

print("Test set AUC: {:.3f}".format(auc_score))   

plt.plot(fpr_svc, tpr_svc, label='ROC Curve', color='cyan')
plt.plot([0,1], [0,1], color='black', linestyle='--')

default_prob = pipe.predict_proba(X_test)[:,1]
confusion_mat = confusion_matrix(y_test, pipe.predict(X_test))
results = classification_report(y_test, pipe.predict(X_test))



return default_prob, confusion_mat

As it can be seen, I have created a simple if-statement where I subtract the AUC from 1 if it is less than 0.5, and this was done for the plot of the ROC curve too, since I would be getting an inverse (or convex) ROC curve rather than a concave one. Before doing this, when inputting my test data into the function I would occasionally yield a test AUC score greater than 0.5, which resulted in a normal concave ROC curve, but mainly they were around 0.4 or as low as 0.3.

enter image description here

The figure on the left corresponds to an AUC score of 0.629, whilst the one on the right corresponds to an AUC score of 0.401.

Therefore, does anybody know what could be the cause of such volatile and unusually low AUC scores for the combination of ETC and logistic regression? From what I've read online, ETC has the tendency of capturing very intricate highly non-linear relationships among variables, which might explain why logistic regression, which is naturally a linear model (?), underperforms compared to the rest. If it helps, my confusion matrix is the following: enter image description here

I would highly appreciate any sort of input or help by anyone.

  • 4
    $\begingroup$ There's no reason to think that the features a tree based model thinks are important will also be important in a logistic regression model. The tree can capture very non-linear relationships, but the regression cannot. $\endgroup$ Commented Aug 10, 2017 at 13:21
  • 3
    $\begingroup$ to @MatthewDrury 's point, an outline of the contrast between random forest and logistic regression wrt feature importance is here stats.stackexchange.com/questions/164048/… $\endgroup$
    – Sycorax
    Commented Aug 10, 2017 at 23:45
  • 2
    $\begingroup$ @Sycorax I was looking for that. Thanks for posting. $\endgroup$ Commented Aug 11, 2017 at 0:06
  • 2
    $\begingroup$ @Sycorax I have fully read your explanation of the random forest implementation as a feature selector for a regression model and I must say that it was outstanding - very detailed! So do you reckon that the fact that I'm obtaining $AUC\leq 0.5$ when using ETC as the feature selector and multiple logistic regression is no surprise at all? $\endgroup$
    – Jayjay95
    Commented Aug 11, 2017 at 13:18
  • 2
    $\begingroup$ No, it's not surprising at all that the extra trees model and the logistic regression model identify different information as being important. While extra trees composes classifiers in a different way from random forest, the core idea, that the model can learn nonlinear relationships, remains the same. $\endgroup$
    – Sycorax
    Commented Aug 11, 2017 at 15:25

1 Answer 1


UPDATE: Sycorax posted the following link in the comments: Can a random forest be used for feature selection in multiple linear regression? deals with this problem and describes why this might not work too well.

Similar explanation: your data/model might suffer from the Curse of dimensionality, as logistic regression is prone to fall to this curse.

Several points: (might be comments with enough reputation)

pipe.fit(X_train, y_train)

Where did you define the training data?

Have you tried class_weight="balanced" for logistic regression? This might produce a different rate of misclassification.

What were the results without the RFE step?

  • $\begingroup$ @M K Apologies for the late reply. Upon doing that I seem to obtain the following confusion matrix: [[170, 130], [15, 9]]. Hence 9 1s (positives) are truly predicted. However, now 130 0s are being misclassified too, and the AUC score is of 0.439, which still seems to be less than 0.5. Do you have any idea why I might be obtaining so many AUC scores that are lower than 0.5? Is logistic regression with the extra tree feature classifier combination really that bad that the model deliberately predicts the wrong classes? $\endgroup$
    – Jayjay95
    Commented Aug 10, 2017 at 22:01
  • $\begingroup$ @Jayjay95: Were the results without RFE better? If not, you could try to limit its output to fewer features, not more. $\endgroup$
    – M K
    Commented Aug 11, 2017 at 9:19
  • $\begingroup$ @M K the results without RFE were essentially the same. The reason why I decided to opt for the implementation of ETC into RFE is because it allowed the grid search process to run significantly faster than without it; when RFE wasn't implemented the pipeline+GridSearchCV procedure with ETC as the feature selection method took 17 hours to fully run , which is criminally slow. In fact, I did it for n=5 and that didn't seem to fix the problem. I might have to accept the fact that using logistic regression as the classifier alongside ETC as the feature selector gives appalling results. $\endgroup$
    – Jayjay95
    Commented Aug 11, 2017 at 13:09
  • $\begingroup$ @Jayjay95: maybe your problem is so highly nonlinear that it's just not possible to find a good result using logistic regression. You might be able to test for this by using a linear SVM and seeing if its results are any better. Another idea: how about using LogisticRegression in feature selection, as well? A third: How about just using ET, or maybe Xgboost, instead? $\endgroup$
    – M K
    Commented Aug 13, 2017 at 7:00

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