Hello,
For my final project model, the outcome is known – in-hospital mortality – and it is binary, so I chose supervised machine learning algorithms (classification or regression). I also want a more interpretable model and one that provides a probability of mortality and not just predicted categories, so I explored a few different models: stepwise logistic regression, Bayesian network, C5 classification tree, CART classification tree. However, when I run these models, I get an AUC of ~0.5. I then ran the ‘Autoclassifer’ node without selecting models. SPSS selected XGBoost Tree, Random Tree, CHAID, and Decision List as the four highest performing models and the AUCs improved to 0.6-0.7 in the testing set. Is my problem that I was selecting the wrong learning algorithms for the data or something else?
Thanks,
Teresa