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Should you standardize categorical variables?
It is common practice to standardize or center variables to make the data more interpretable in simple slopes analysis; however, categorical variables should never be standardized or centered. This test can be used with all coding systems.
Should I normalize binary features?
Some algorithms are better at dealing with unnormalized features than others, I think, but in general if your features have vastly different scales you could get in trouble. So normalizing to the range 0 – 1 is sensible. Sometimes it could be valuable to scale some parts of the feature space differently than others.
When do categorical variables need to be normalized?
Normalization/standardization of features is done to bring all features to a similar scale. When you one hot encode categorical variables they are either 0/1 hence there is not much scale difference like 10~1000 hence there is no need to apply techniques for normalization/standardization.
Why is it important to handle categorical variables?
Handling categorical variables is an important step for feature engineering. New variables can be formed by categorical variables and get more insight into the dataset. The complete code of the above implementation is available in the AIM’s GitHub repository. Please visit this link to find the notebook of the above codes.
When to standardize before or after categorical encoding?
You generally want to standardize all your features so it would be done after the encoding (that is assuming that you want to standardize to begin with, considering that there are some machine learning algorithms that do not need features to be standardized to work well). So there is 50/50 voting on whether to standardize data or not.
What is the difference between categorical, ordinal and…?
A purely categorical variable is one that simply allows you to assign categories but you cannot clearly order the variables. If the variable has a clear ordering, then that variable would be an ordinal variable, as described below.