Encoding Before vs After Train_Test_Split?

Answer: Encode after train_test_split to prevent data leakage and ensure the model generalizes well to unseen data.

When preparing data for machine learning models, the timing of encoding categorical variables in relation to splitting the dataset into training and testing sets can significantly impact the model’s performance. Encoding can be performed using various methods such as one-hot encoding, label encoding, or target encoding.

Aspect	Encoding Before Split	Encoding After Split
Data Leakage	High risk of data leakage, as information from the test set may influence the encoding.	Reduces risk of data leakage by ensuring the encoding is based solely on the training data.
Model Generalization	May lead to overfitting, as the model is exposed to the entire dataset’s information.	Promotes better model generalization by learning from only the training set’s distribution.
Consistency	Ensures encoding consistency across the entire dataset.	Requires careful handling to maintain encoding consistency between training and test sets.
Practicality	Simplifies preprocessing by encoding the entire dataset at once.	More complex but safer approach, involving separate encoding steps for training and test sets.

Conclusion

While encoding before splitting the dataset might seem convenient and ensures consistent encoding across the entire dataset, it introduces a high risk of data leakage and may compromise the model’s ability to generalize to unseen data. Encoding after performing the train_test_split is a best practice that helps prevent data leakage, ensuring that the information used to train the model is strictly separated from the test set. This approach supports the development of more robust models by promoting better generalization and preventing overfitting.