What is Target Encoding?
Target encoding is a powerful technique used in feature engineering, particularly for categorical variables, in the field of machine learning. It transforms categorical data into a numerical format by replacing each category with the average of the target variable for that category.
How Target Encoding Works
- Calculate the Mean: For each category in the categorical feature, compute the mean of the target variable.
- Replace Categories: Substitute the original categories in the dataset with their corresponding mean values.
- Handle Overfitting: To mitigate overfitting, techniques such as adding noise, smoothing, or performing cross-validation can be employed.
Advantages of Target Encoding
- Preserves Information: Retains more information compared to one-hot encoding, especially for high cardinality features.
- Improves Model Performance: Often leads to enhanced predictive performance for many machine learning models.
Considerations
While target encoding can improve model accuracy, it's essential to apply it cautiously. Ensure that the encoding is done on the training set separately from the test set to prevent data leakage.