# Data

Schema

<figure><img src="/files/4LXAGoF6LKuGCf7hFeS3" alt=""><figcaption></figcaption></figure>

On Data settings page you can configure or change the configuration for the table schema for the model's dataset. Here it is possible to point on the dataset schema the mandatory NannyML data like:

* Timestamp: The date and time the prediction was made.
* Identifier: Unique identifier for each row on the dataset.
* Target: The actual outcome of what your model predicted.
* Predicted probability: The probabilities assigned by a machine learning model regarding the chance that a positive event materializes

It is possible also to point columns on the dataset schema for non mandatory NannyML data like:

* Prediction: The prediction made by the model.
* Segmented by: Segmentation allows you to split your data into groups and analyze them separately

<figure><img src="/files/TLH9s5rOTMUJiF0BqKLr" alt=""><figcaption></figcaption></figure>

You can find more information about the meaning of the mandatory and non mandatory information on the NannyML Cloud by clicking the info icon beside the information label.

{% hint style="info" %}
**Segmentation** allows you to split your data into groups and analyze them separately. Each segmentation column provides a separate group of segments that are not combined.

For example having 'gender' and 'region' as segmentation columns might result in 'gender: male', 'gender: female' and 'region: US' segments but there won't be a 'female-US' segment. If you want to analyze combined segments, you should create a new column that combines the segments you want to analyze together.
{% endhint %}

It is also possible to assign the columns on the schema dataset to the NannyML Cloud information directly from the table view.

<figure><img src="/files/vGJxwBuSLBThz3AkJoAx" alt=""><figcaption></figcaption></figure>

Each column can have a special Column flag field. Right now it is possible to use this field to add segmentation to the columns.

### Data Sources

Under datasets, you can manually add more analysis and target data.

<figure><img src="/files/WoZ2Wx6LcueiNqajsM1S" alt=""><figcaption></figcaption></figure>

The new datasets can be imported from:

<figure><img src="/files/w9JED7JIFhcx6I6AbECO" alt=""><figcaption></figcaption></figure>

* Azure blob storage
* Local file (the maximum size for the file is 200MB)
* Public link
* Amazon S3

New datasets can also be imported using NannyML SDK. Check out the documentation [here](https://docs.nannyml.com/cloud/nannyml-cloud-sdk).


---

# Agent Instructions: 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:

```
GET https://docs.nannyml.com/cloud/v0.24.0/product-tour/model-side-panel/model-settings/data.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
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.