The Most Overlooked Problem With One-Hot Encoding

With one-hot encoding, we introduce a big problem in the data.

When we one-hot encode categorical data, we unknowingly introduce perfect multicollinearity.

Multicollinearity arises when two or more features can predict another feature.

As the sum of one-hot encoded features is always 1, it leads to perfect multicollinearity.

This is often called the Dummy Variable Trap.

It is bad because:

• The model has redundant features

• Regressions coefficients aren’t reliable in the presence of multicollinearity, etc.

So how to resolve this?

The solution is simple.

Drop any arbitrary feature from the one-hot encoded features.

This instantly mitigates multicollinearity and breaks the linear relationship which existed before.

So remember...

Whenever we one-hot encode categorical data, it introduces multicollinearity.

To avoid this, drop one column and proceed ahead.

👉 Over to you: What are some other problems with one-hot encoding?

👉 Read what others are saying about this post on LinkedIn and Twitter.

👉 Tell the world what makes this newsletter special for you by leaving a review here :)

👉 If you liked this post, don’t forget to leave a like ❤️. It helps more people discover this newsletter on Substack and tells me that you appreciate reading these daily insights. The button is located towards the bottom of this email.

👉 If you love reading this newsletter, feel free to share it with friends!

👉 Sponsor the Daily Dose of Data Science Newsletter. More info here: Sponsorship details.

Find the code for my tips here: GitHub.

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.