Have you ever wondered why using OneHotEncoder yields better results than get.dummies/reindex in certain machine learning models? I stumbled upon this curious phenomenon while working on a project, and I’d like to share my findings with you.
In my experiment, I used a Gradient Boosting Regressor model with a ColumnTransformer that employed OneHotEncoder for categorical variables. To my surprise, this setup produced better scores than when I used get.dummies/reindex to encode the categorical variables. But why?
After digging deeper, I realized that OneHotEncoder is more efficient in handling categorical variables, especially when combined with a ColumnTransformer. This is because OneHotEncoder creates a separate binary feature for each category, allowing the model to capture more nuanced relationships between the variables. On the other hand, get.dummies/reindex can lead to the curse of dimensionality, making it harder for the model to generalize.
So, what does this mean for us data scientists? It’s essential to choose the right encoding technique for our categorical variables, depending on the specific problem and model we’re working with. OneHotEncoder might be a better option when dealing with complex categorical relationships, while get.dummies/reindex could be sufficient for simpler cases.
What are your thoughts on this? Have you encountered similar experiences with OneHotEncoder and get.dummies/reindex?