ML newbie here, currently looking at a binary classification problem. I have quite a good number of training data (easily over 50k) which consists of both numeric and categorical data. The categorical data consists of both ordinal and nominal types.
Here's the problem. I am unsure of what is the most proper way of encoding the categorical data, and what are the factors I should consider when deciding the encoding method. I have came across several encoding methods, which can be summarized in this article.
As additional information, I am thinking of using logistic regression and random forest as my first test classifiers. I have read that certain encoding methods are more suitable for certain types of classifiers. Hope to have more insight on that matter as well.
I hope that you guys/girls can lend me a helping hand. Thank you very much in advance.
Due to P&C, I cannot share instances of the data, however these are examples of the categorical features, and the number of different data for each feature:
- Country (nominal) (40)
- Job Grade (ordinal) (8)
- Year/Quarter Joined (can be ordinal or nominal) (15)
- Department/Business Unit (nominal) (10)
Library used: scikit-learn