[DS]Data Preprocessing - Choosing Between One-Hot Encoding and Label Encoding in Random Forest

Which should you choose between One-Hot Encoding and Label Encoding in Random Forest?

Let’s judge based on the concepts of each.

Concepts

One-Hot Encoding

One-Hot Encoding creates new features for each unique category value.

Label Encoding

Here, each unique category value is replaced with a continuous number. It is computationally more efficient, but care must be taken as continuous numbers can mislead the model into interpreting a ranking among values.

You can easily understand this with the image below.

Source: https://www.linkedin.com/posts/uditsaini_one-hot-encoding-vs-label-encoding-categorical-activity-7124082974827888640-vXO7/

Conclusion

As you might have noticed while summarizing the concepts, Label Encoding converts categorical data into integers, but this can cause the model to misinterpret the order among these integer values. Therefore, it is appropriate to apply the One-Hot Encoding method in Random Forest.