Frequency Encoding vs One Hot 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 one hot encoding when working with machine learning datasets that include categorical features like colors, countries, or product types, as most algorithms cannot process raw text labels directly. 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

One Hot Encoding

Developers should learn One Hot Encoding when working with machine learning datasets that include categorical features like colors, countries, or product types, as most algorithms cannot process raw text labels directly

Pros

• It is essential for tasks like classification, regression, and deep learning to avoid misleading ordinal relationships, ensuring each category is treated as a distinct entity without implying any order or hierarchy
• Related to: data-preprocessing, feature-engineering

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 One Hot Encoding if: You prioritize it is essential for tasks like classification, regression, and deep learning to avoid misleading ordinal relationships, ensuring each category is treated as a distinct entity without implying any order or hierarchy 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