Machine Learning – Should Feature Selection be Done Before or After Encoding?

Question

Should I apply feature Scaling and Selection before or after the One Hot Encoding/Label Encoding?

Please Correct me if I'm Wrong-

1. Deal with Outliers
2. Impute missing Values
3. Label Encode/One Hot encode categorical values
4. Apply Dimensionality Reduction
5. Apply Feature Selection

Please correct if I'm wrong.

Answer 1

The mentioned steps are correct.

Feature scaling (min/max, mean/stdev) is for numerical values so it doesn't matter to be before or after label encoding; but keep it in mind that you SHOULD NOT do scaling on encoded categorical features.

For dimensionality reduction or feature selection, you need to have numerical values; so you should do them after label encoding.