Is Categorical Feature Encoding Always Necessary Before Training ML Models?
Is Categorical Feature Encoding Always Necessary Before Training ML Models?
If not, when is it not needed?
When data contains categorical features, they may need special attention at times. This is because many algorithms require numerical data to work with.
Thus, when dealing with such datasets, it becomes crucial to handle these features appropriately to ensure accurate and meaningful analysis.
For instance, one common approach is to use one-hot encoding, as shown below:
Encoding categorical data allows algorithms to process them effectively.
But is it always necessary?
While encoding categorical data is often crucial, knowing when to do it is also equally important.
The following visual depicts which algorithms need categorical data encoding and which don’t.
As shown above, many ML algorithms typically work well even without categorical data encoding. These include decision trees, random forests, naive bayes, gradient boosting, and more.
Consider a decision tree, for instance. It can split the data based on exact categorical feature values. This makes categorical feature encoding an unnecessary step.
Thus, it's important to understand the nature of your data and the algorithm you intend to use.
You may never need to encode categorical data if the algorithm is insensitive to it.
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I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn and Twitter.
I think that sklearn is unable to deal with pure categorical data, so if using that library, you do need to encode categorical data even for decision trees etc. There are other libraries, I think maybe one called H20 which does deal with categorical correctly.