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:

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
)

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].

ModelTaskParams

RowDataSource

⌘I

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

type

n_clusters

centroids

columns

monitor_components

classmethod validate_columns()

Returns

classmethod validate_n_clusters()

Returns

model_config

​Examples

​type

​n_clusters

​centroids

​columns

​monitor_components

​classmethod validate_columns()

​Returns

​classmethod validate_n_clusters()

​Returns

​model_config

Examples

type

n_clusters

centroids

columns

monitor_components

classmethod validate_columns()

Returns

classmethod validate_n_clusters()

Returns

model_config