Have you ever wondered why OneHotEncoder gives better results than get.dummies/reindex in certain situations? I know I have. Recently, I stumbled upon a Reddit post that sparked my curiosity. The user, /u/Due-Duty961, shared their experience with using OneHotEncoder and get.dummies/reindex for categorical data transformation. They noticed that OneHotEncoder performed better than get.dummies/reindex, and I’m going to break down why that might be the case.
The key difference lies in how these two methods handle categorical data. OneHotEncoder is a more sophisticated approach that creates a binary vector for each category, whereas get.dummies/reindex simply creates a new column for each category. This subtle difference can have a significant impact on the performance of your model.
In the Reddit post, the user used a Pipeline with a ColumnTransformer to apply OneHotEncoder to the categorical columns. This allowed the model to learn more nuanced relationships between the categorical variables and the target variable. On the other hand, get.dummies/reindex can lead to the curse of dimensionality, where the model becomes overwhelmed by the large number of new columns.
So, the next time you’re working with categorical data, consider using OneHotEncoder instead of get.dummies/reindex. You might be surprised at the improvement in performance.
What’s your experience with these methods? Do you have any tips to share?