What is the primary purpose of using a ‘drop=’ argument in OneHotEncoder when handling binary categorical features?
Data ScienceMedium
Data Science — Medium
What is the primary purpose of using a ‘drop=’ argument in OneHotEncoder when handling binary categorical features?
Explanation
When encoding a binary feature, two columns are created (e.g., Is_Yes and Is_No). These are perfectly collinear (one is the inverse of the other). Dropping one column (the 'dummy variable trap') is essential for linear models to avoid multicollinearity, which can lead to unstable coefficient estimates.
