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

# Model Schema Editing

> Learn how to modify numeric ranges, edit categorical features, and add metadata columns to keep your model schema aligned with evolving production data.

## Overview

This guide explains how to edit your model's schema in Fiddler to better align with production data. Schema editing helps you maintain accurate monitoring as your data evolves.

Key capabilities

* Adjust numeric feature ranges when real-world data deviates from your original sample data
* Edit categorical feature values to add or remove categories as new patterns emerge
* Add metadata columns to include additional contextual information for improved insights
* Customize histogram bin boundaries for numerical columns to better represent data distributions

### Adjusting numeric feature ranges

**Access the Schema tab**

1. Navigate to the Model Page of your desired model
2. Select the Schema tab

**Edit numeric column range**

1. Find the numeric column you want to adjust
2. Select the edit icon (✏️) next to the column name
3. In the dialog box, modify the minimum and/or maximum values
4. Select Update to save your changes

**Impact of changes**

* **Data drift metrics**: Changes apply to all data, including historical data
  * A job will run to recalculate aggregates and update metrics
* **Data integrity metrics**: Changes only apply to new data going forward

#### Editing histogram bins

You can customize the histogram bin boundaries for numerical columns to better represent your data distribution (e.g., quantile-based bins instead of uniform).

**Edit bins for a numeric column**

1. Find the numeric column you want to adjust
2. Select the edit icon (✏️) next to the column name
3. In the dialog box, enter comma-separated bin boundary values in the **Bins** field
   * Example: `350, 450, 550, 650, 750, 850`
   * Values must be strictly increasing, span the column's \[min, max] range, and have at most 16 boundary values (15 bins)
4. To revert to auto-generated uniform bins, clear the Bins field
5. Select Update to save your changes

**Impact of changes**

* **Data drift metrics**: Changes apply to all data, including historical data
  * A job will run to recalculate aggregates and update histogram metrics
* **Feature distribution charts**: Histogram visualizations will use the new bin boundaries

#### Editing categorical variables

**Access the Schema tab**

1. Navigate to the Model Page of your desired model
2. Select the **Schema** tab

**Edit categorical column**

1. Locate the categorical column you want to modify
2. Select the edit icon (✏️) next to the column name
3. Add or remove categories as needed
4. Select Update to save your changes

**Impact of changes**

* For both data drift and data integrity metrics:
  * Changes only apply to new data going forward
  * Historical data remains unchanged

#### Adding metadata columns

**Access the Schema tab**

1. Navigate to the Model Page of your desired model
2. Select the Schema tab

**Add a Metadata Column**

1. Select Add Metadata
2. Provide the required information:
   * Column Name: Specify the name of the new metadata column
   * Data Type: Choose a data type (integer, float, string, or boolean)
   * Range: For numeric types, define minimum and maximum values
3. Select Add to save

**Impact of Changes**

* New metadata columns are effective immediately for new data

### Best Practices

* Analyze production data to set realistic range values and identify useful metadata columns
* Monitor metrics after adjustments to ensure changes effectively address your needs
* Use chart annotations for transparency to maintain a clear history of schema changes

<Warning>
  Schema updates that change numeric column properties (min, max, bins) trigger re-computation of historical aggregates. Models with more than 10 million events in any environment are not eligible for these updates. Contact support for assistance with large-scale schema changes.
</Warning>

### Frequently Asked Questions

**Can I change column names or data types?**

No, changing column names or data types is not supported.

**What if I make a mistake?**

You can edit the values again and save the updated schema.

**How long do changes take to apply?**

Application time depends on dataset size and complexity. For example, processing 10 million rows over six months takes approximately 12 minutes.

**Can I delete a metadata column?**

No, metadata columns cannot be deleted once added.

**Can I customize histogram bins?**

Yes, you can set custom bin boundaries for numerical columns via the Bins field in the schema editor, or programmatically via the Python client using `model.update()`. Leave the field empty to use auto-generated uniform bins.

**What happens if I add a category that doesn't exist in the data?**

The category will be listed but won't impact existing calculations.