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!