I am training a classification model. I used KNN, decision tree, random forest, and logistics regression. The classification is around 65% yes: and 45% No, showing no imbalance data. However, when I train my data, all the models will give the same result as the image below.
The model classifies everything into Yes. I try to change some parts of the learner but the kohen’s kappa never exceeds 0.02. My random forest also classifies 100% yes in the first node for all models. My data are all correct and it contains both integer and string variables. I don’t know what is wrong with my models. Is it because there is a very low correlation between my independent variables and the dependent variable?
Any help would be appreciated. Can’t share the workflow because it contains confidential information.
