> For the complete documentation index, see [llms.txt](https://docs.everesteer.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.everesteer.ai/data/splits-and-obfuscation.md).

# Splits & obfuscation

One obfuscated feature set with rank-normalised targets, split three ways.

| Split        | Targets     | What it is for                                                                                                                                      |
| ------------ | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------- |
| `train`      | **Labeled** | Labeled history and the bulk of the data. Fit your model here, and carve a hold-out from it to score yourself offline before spending a submission. |
| `validation` | Blanked     | The day-0 practice set: same features, targets withheld. Scored server-side.                                                                        |
| `live`       | Blanked     | The currently open round. Same features, targets withheld.                                                                                          |

Your predictions on the two blanked splits are what the boards score, server-side, against an answer key you never receive.

{% hint style="danger" %}
Predicting on the wrong one of `validation` / `live` gives a frame whose ids do not join the open round. `download_dataset(split='live')` serves whichever round is currently open; `split='validation'` serves the practice board that runs before round 1.
{% endhint %}

## Obfuscation

{% stepper %}
{% step %}

### Rank

Every feature is ranked across the cross-section of that exped.
{% endstep %}

{% step %}

### Bin

The ranks are quintile-binned.
{% endstep %}

{% step %}

### Rename

The original name and the original value are not in the file. Raw institutional data is never exposed through the API or the dataset.
{% endstep %}
{% endstepper %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.everesteer.ai/data/splits-and-obfuscation.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.