# Repeatable Sampling

> Deterministic, reproducible train/validation/test splits that prevent data leakage

- **Category**: Machine Learning
- **Subcategory**: Training Process and Optimization
- **Canonical URL**: https://designpattern.fyi/patterns/repeatable-sampling/

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## Description
**Intent**: Ensure reproducible splits across runs and prevent information leakage when data has natural groupings that shouldn't be split across train and test sets.

**Context**: A fresh random shuffle for every split isn't reproducible across runs. Naive row-level splits can leak information when data has natural groupings (multiple rows for same customer), putting correlated records in both train and test and inflating apparent performance.

**Solution**: Use a deterministic mechanism—typically hashing a stable identifier like customer ID into a fixed numeric range—to assign every record connected to the same real-world entity to the same split, consistently every time. This makes splits reproducible and prevents leakage by keeping related records together.



## Use Cases
- Customer data with multiple transactions per customer
- User activity data with multiple sessions per user
- Medical data with multiple visits per patient
- Any data with natural groupings that shouldn't be split






## Trade-offs


### Advantages

- Exactly reproducible splits across runs and environments

- Prevents information leakage from grouped data

- Deterministic and reliable

- Enables proper evaluation without inflated metrics




### Considerations & Drawbacks

- Depends on having stable, well-distributed identifier

- Picking wrong grouping key defeats the purpose

- May need adjustment if hash doesn't distribute evenly

- Requires understanding of data's natural groupings







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