# Multivariate

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

## type *: Literal\['FROM\_COLUMNS']*

## n\_clusters *: int | None*

## centroids *: List | None*

## columns *: List\[str]*

## monitor\_components *: bool*

## *classmethod* validate\_columns(value)

**Return type:** *List*\[str]

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

**Return type:** int


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