This seems like an easy question but I haven't been able to find a definitive source for this or questions that address this topic directly.
When applying a classification algorithm, should you apply one-hot encoding on Likert-scale features? Take for example the features in IBM's attrition dataset:
JobSatisfaction 1 'Low' 2 'Medium' 3 'High' 4 'Very High'
I know that one-hot encoding should be done on categorical values (e.g., gender) but I don't know what to do about Likert scale items, which can be interpreted as ordinal, nominal or even continuous (though I tend to disagree with the latter).
I'm conducting Logistic Regression on this data set without creating dummies for the Likert features--at best my AUC is .59. I checked the approaches of others on Kaggle and those that achieved an AUC of .7 to .8 encoded some of the Likert features.