> ## Documentation Index
> Fetch the complete documentation index at: https://docs.overmindlab.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Separate training and held-out data

> Save group-preserving train, development, calibration and final roles.

Use a saved partition when repeated observations, conversations or related source examples must stay together. Begin with an inspected, prepared source cell and a known task contract.

Start with the [fraud example dataset](/reference/example-dataset.md) from [Prepare data](/how-to/prepare-data.md). The agent inspects related transactions and chooses an appropriate split.

Create training, development and held-out evaluation splits in Overmind for this fraud dataset: [https://huggingface.co/datasets/apex0data/ecommerce-fraud-detection-synthetic-10k-sampl](https://huggingface.co/datasets/apex0data/ecommerce-fraud-detection-synthetic-10k-sampl). Check for overlap that could make the evaluation misleading, then explain the split sizes and label balance.

**What you get:** A saved partition recipe, actual role counts and exact cells without shared groups.

The steps below describe the workflow your agent follows and the results to verify.

1. Identify related records: source groups, conversations, repeated inputs and synthetic descendants. Declare additional grouping fields from evidence rather than guessing from column names.
2. Choose role fractions and a fixed seed. Use **train** for fitting, **development** for recipe/checkpoint decisions, **calibration** for fitting calibration and **final** for the independent comparison. Leave unused roles out of the recipe.
3. Call `create_data_partition` with `name`, `request_key`, `source_cell` and `recipe`. The recipe accepts `seed`, `fractions`, `group_by`, optional `stratify_by` and explicit `holdouts`.
4. Follow the returned job through construction. Inspect actual role counts and group assignment; group sizes may prevent exact requested fractions.
5. Verify the output cells against their intended consumer. Train/development roles receive training intent, while calibration/final roles receive evaluation intent. Native decision rows in calibration/final members are projected into input-only decisions and separate references. Other row formats are retained; changing intent alone does not convert them.
6. Save the role cell IDs and use them explicitly in later jobs.

Do not deduplicate repeated observations merely to achieve a ratio. Preserve their weights and provenance. Missing declared group fields are an error to resolve, not a reason to silently drop the grouping rule.

Use `retry_data_partition` only after inspecting a failed construction and the supported recovery action. It resumes the saved recipe rather than inventing another split.

For a data-first modelling project, retain the task definition and exact role cells in `save_training_plan`. Final answers must not guide recipe selection. See [Data lineage and held-out evidence](/explanation/data-lineage.md).


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.