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The Most Overlooked Problem With One-Hot Encoding
Hint: This is NOT about sparse data representation.
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.
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?
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