# Hashed Feature

> Bounded representation for high-cardinality categorical data using hash functions

- **Category**: Machine Learning
- **Subcategory**: Data and Feature Representation
- **Canonical URL**: https://designpattern.fyi/machine_learning/hashed-feature/

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## Description
**Intent**: Handle categorical fields with very high or open-ended cardinality (user IDs, search queries, zip codes, free-text tags) that would blow up vocabulary-based encoding sizes and cause out-of-vocabulary issues in production.

**Context**: When categorical features have unbounded or constantly growing vocabularies, expected cold-start categories in production, or tight memory budgets, traditional one-hot encoding becomes impractical.

**Solution**: Apply a hash function to the category value and take the result modulo a fixed number of buckets, producing a bounded-size representation regardless of how many distinct values exist. There's no vocabulary to store or maintain, and out-of-vocabulary values hash into a bucket like everything else. The cost is potential collisions where unrelated categories land in the same bucket.



## Use Cases
- User IDs or product IDs in recommendation systems
- Search queries or free-text tags in classification
- Zip codes or geographic identifiers with high cardinality
- Any categorical feature with unbounded or rapidly growing vocabulary






## Trade-offs


### Advantages

- Bounded, low-maintenance representation regardless of cardinality

- No vocabulary to store or maintain

- Handles out-of-vocabulary values gracefully

- Memory-efficient for high-cardinality features




### Considerations & Drawbacks

- Non-reversible mapping (loss of interpretability)

- Collisions can inject noise and hurt accuracy if too few buckets

- Less precise than full vocabulary encoding







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**Reference**: [Original Source](https://github.com/GoogleCloudPlatform/ml-design-patterns)