Categorical Encoding vs Frequency Encoding

Developers should learn categorical encoding when working with machine learning models, as most algorithms (e meets 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. Here's our take.

Categorical Encoding

Nice Pick

Developers should learn categorical encoding when working with machine learning models, as most algorithms (e

Pros

• g
• Related to: data-preprocessing, feature-engineering

Cons

• Specific tradeoffs depend on your use case

Frequency 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

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

The Verdict

Use Categorical Encoding if: You want g and can live with specific tradeoffs depend on your use case.

Use Frequency Encoding if: You prioritize it's ideal for datasets with many unique categories, such as zip codes or product ids, where frequency might correlate with the target variable over what Categorical Encoding offers.

The Bottom Line

Categorical Encoding wins

Developers should learn categorical encoding when working with machine learning models, as most algorithms (e