Multivariate

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:

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:

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

Attributes

Literal['FROM_COLUMNS']

Type discriminator; fixed as CustomFeatureType.FROM_COLUMNS.

int | None

Number of k-means clusters used for drift detection (default 5).

List | None

Cluster centroids in the embedded space; populated during training.

List[str]

Names of the input columns combined into this feature; at least two required.

bool

Whether to monitor each column individually for drift (default False).

classmethod validate_columns()

Returns

List[str]

classmethod validate_n_clusters()

Returns

int

Fiddler Python Client SDK

Fiddler Evals SDK

Fiddler OTel SDK

Fiddler LangGraph SDK

Fiddler LangChain SDK

Fiddler Google ADK SDK

Fiddler Strands Agent SDK

Fiddler OTel JS SDK

Fiddler LangGraph JS SDK

Fiddler LangChain JS SDK

Examples

Attributes

classmethod validate_columns()

Returns

classmethod validate_n_clusters()

Returns

​Examples

​Attributes

​classmethod validate_columns()

​Returns

​classmethod validate_n_clusters()

​Returns

Examples

Attributes

classmethod validate_columns()

Returns

classmethod validate_n_clusters()

Returns