> ## 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.

# CustomFeature

> Base class for all custom feature types in Fiddler models.

Base class for all custom feature types in Fiddler models.

CustomFeature provides the foundation for creating specialized feature types
that enhance model monitoring and analysis. Custom features allow you to define
derived metrics, embeddings, and enrichments that extend beyond basic model
inputs and outputs for advanced drift detection and analysis.

This is an abstract base class that should not be instantiated directly.
Instead, use one of its concrete subclasses: Multivariate, VectorFeature,
TextEmbedding, ImageEmbedding, or Enrichment.

## Examples

Creating a multivariate feature from multiple columns:

```python theme={null}
feature = CustomFeature.from_columns(
    custom_name="user_behavior_cluster",
    cols=["clicks", "views", "time_spent"],
    n_clusters=5
)
```

Creating a custom feature from a dictionary:

```python theme={null}
feature_dict = {
    "name": "text_sentiment",
    "type": "FROM_TEXT_EMBEDDING",
    "column": "embedding_col",
    "source_column": "review_text"
}
feature = CustomFeature.from_dict(feature_dict)
```

## name

## type

## model\_config

Configuration for the model, should be a dictionary conforming to \[ConfigDict]\[pydantic.config.ConfigDict].

## *classmethod* from\_columns()

### Returns

[`Multivariate`](/sdk-api/python-client/multivariate)

## *classmethod* from\_dict()

### Returns

`Any`

## to\_dict()

### Returns

`Dict[str, Any]`