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One-hot encoding is probably the most popular way of dealing with nominal categorical variables in machine learning models. However, target encoding offers a powerful alternative with several advantages.

Target encoding is where each category is replaced with the average value of the target variable for that category. For instance, in a binary classification task, each category in a categorical feature is encoded with the probability of the positive class occurring given that category.

Target encoding offers several advantages over other methods of dealing with categorical variables:

• Unlike one-hot encoding, which expands categorical variables into binary columns, target encoding retains the original information in a continuous form. This can be particularly useful when dealing with high-cardinality categorical variables.
• Target encoding does not increase the dimensionality of the dataset (compared to one-hot encoding. This is important when working with large datasets as it can help mitigate the curse of dimensionality and improve computational efficiency.
• Target encoding is robust to rare or unseen categories in the test dataset (e.g., with smoothing). Since it is based on statistical measures such as means, it does not suffer from the problem of missing mappings that one-hot encoding might encounter.
• By encoding categories based on their relationship with the target variable, target encoding can capture valuable information about any underlying patterns. This can potentially improve the performance of machine learning models, especially in predictive tasks.

Despite its advantages, target encoding also comes with some caveats. It is sensitive to overfitting, especially when dealing with small or imbalanced datasets. To mitigate this, techniques such as cross-validation or smoothing can be applied.

Overall, target encoding offers a balance between simplicity, interpretability, and effectiveness in capturing the information encoded in categorical variables.