AUC - Logistic Regression versus LDA, and Naive Bayes everyone!
I am a newbie on machine learning, and I am now interested on classification modeling.
I used logistic regression, linear discriminant analysis (LDA), and naive Bayes on my notebook DataCamp Certification - Travel Insurance as a tool to exploratory data analysis. A thing that caught my attention is the difference between AUC of the three models, more specific between logistic regression and the other tow techniques. I use the Yardstick to calculate this measure.
Well, I am trying to apply this same strategy on Titanic - Machine Learning from Disaster. When I model on my notebook the data with logistic regression, I was surprised by a AUC very low (0.1464) despite my score of 0.7584 on the competition.
It is important to say that some terms on the first notebook show a low p.value. Differently, the second notebook, all terms show p.value < 0.05.
So, I would like to understand this situation because it is worries me a lot!
Thank you all for your time!

It is unintended behaviour by yardstick::roc_auc; if we used another library to compute AUC-ROC, e.g. MLmetrics::AUC(y_true=data_select_pred$Survived, y_pred=data_select_pred$ProbSurvived), we would get an AUC-ROC value of ~0.8536 that matches 1 minus the value you currently get. In general, AUC-ROC values significantly below 0.5 are very often related to coding error/label inversion. The CV.SE thread on: Can AUC-ROC be between 0-0.5? explores this further.
Please consider opening an issue about this in yardstick git repo. There is a similar issue mentioned in September 2021 which I suspect affects you too.
