> ## 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.

# Multivariate

> Represents custom features derived from multiple columns using clustering analysis.

Represents custom features derived from multiple columns using clustering analysis.

Multivariate features combine multiple numeric columns into a single derived feature
using k-means clustering algorithms. This enables monitoring of multivariate drift
and detecting unusual combinations that might not be apparent when monitoring
columns individually.

The feature type is automatically set to CustomFeatureType.FROM\_COLUMNS and uses
clustering to group similar combinations of column values for drift detection.

## Examples

Creating a user behavior multivariate feature:

```python theme={null}
behavior_feature = Multivariate(
    name="user_engagement_cluster",
    columns=["page_views", "session_duration", "clicks"],
    n_clusters=8,
    monitor_components=True
)
```

Creating a system performance multivariate feature:

```python theme={null}
perf_feature = Multivariate(
    name="system_health",
    columns=["cpu_usage", "memory_usage", "response_time"],
    n_clusters=5,
    monitor_components=False
)
```

## type

## n\_clusters

## centroids

## columns

## monitor\_components

## *classmethod* validate\_columns()

### Returns

`List[str]`

## *classmethod* validate\_n\_clusters()

### Returns

`int`

## model\_config

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