Ordinal Encoding vs Target Encoding

Developers should use ordinal encoding when working with categorical features that have a clear ranking, such as education levels (e 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.

Ordinal Encoding

Nice Pick

Developers should use ordinal encoding when working with categorical features that have a clear ranking, such as education levels (e

Pros

• g
• Related to: categorical-encoding, feature-engineering

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 Ordinal Encoding if: You want g 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 Ordinal Encoding offers.

The Bottom Line

Ordinal Encoding wins

Developers should use ordinal encoding when working with categorical features that have a clear ranking, such as education levels (e