Frequency Encoding vs Target Encoding

Developers should learn frequency encoding when working with categorical data in machine learning models, especially for tree-based algorithms like decision trees or gradient boosting, as it can improve model performance by reducing dimensionality compared to one-hot encoding meets developers should learn target encoding when working with categorical data that has many unique values (high cardinality), as traditional one-hot encoding can lead to sparse, high-dimensional datasets. Here's our take.

Frequency Encoding

Nice Pick

Developers should learn frequency encoding when working with categorical data in machine learning models, especially for tree-based algorithms like decision trees or gradient boosting, as it can improve model performance by reducing dimensionality compared to one-hot encoding

Pros

• It's ideal for datasets with many unique categories, such as zip codes or product IDs, where frequency might correlate with the target variable
• Related to: feature-engineering, categorical-encoding

Cons

• Specific tradeoffs depend on your use case

Target Encoding

Developers should learn target encoding when working with categorical data that has many unique values (high cardinality), as traditional one-hot encoding can lead to sparse, high-dimensional datasets

Pros

• It is especially useful in competitions like Kaggle or in production models for tabular data, such as predicting customer churn or sales, where it can capture meaningful patterns without excessive dimensionality
• Related to: feature-engineering, categorical-encoding

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Frequency Encoding if: You want it's ideal for datasets with many unique categories, such as zip codes or product ids, where frequency might correlate with the target variable and can live with specific tradeoffs depend on your use case.

Use Target Encoding if: You prioritize it is especially useful in competitions like kaggle or in production models for tabular data, such as predicting customer churn or sales, where it can capture meaningful patterns without excessive dimensionality over what Frequency Encoding offers.

The Bottom Line

Frequency Encoding wins

Developers should learn frequency encoding when working with categorical data in machine learning models, especially for tree-based algorithms like decision trees or gradient boosting, as it can improve model performance by reducing dimensionality compared to one-hot encoding