Have you ever wondered why OneHotEncoder seems to give better results than get.dummies/reindex in certain machine learning models? I recently stumbled upon this phenomenon, and I’m excited to share my findings with you.
In my experiment, I used a Gradient Boosting Regressor (GBR) with a custom preprocessor that employed OneHotEncoder to handle categorical variables. To my surprise, this approach yielded a better score than when I used get.dummies and reindex to encode the categorical variables.
But why is this the case? After digging deeper, I realized that OneHotEncoder is more efficient in handling categorical variables, especially when there are many categories involved. This is because OneHotEncoder creates a separate binary feature for each category, which allows the model to capture more nuanced relationships between the variables.
On the other hand, get.dummies/reindex can lead to the curse of dimensionality, especially when dealing with high-cardinality categorical variables. This can result in a model that’s prone to overfitting and performs poorly on unseen data.
In conclusion, if you’re working with categorical variables in your machine learning model, it’s worth considering OneHotEncoder as an alternative to get.dummies/reindex. You might be surprised at the improvement in performance you can achieve.
What’s your experience with encoding categorical variables? Have you encountered any similar issues or successes?