ModelTaskParams

ModelTaskParams defines task-specific parameters that control how models are evaluated and monitored within Fiddler. Different model types (classification, regression, ranking) require different parameters to properly compute metrics and perform analysis. These parameters are essential for accurate metric computation, proper baseline establishment, and meaningful performance monitoring across different model types and use cases.

Examples

Configuration for binary classification:

binary_params = ModelTaskParams(
    binary_classification_threshold=0.5,
    target_class_order=["negative", "positive"]
)

Configuration for multi-class classification with class weights:

multiclass_params = ModelTaskParams(
    target_class_order=["class_a", "class_b", "class_c"],
    class_weights=[0.3, 0.5, 0.2],
    weighted_ref_histograms=True
)

Configuration for ranking models:

ranking_params = ModelTaskParams(
    group_by="query_id",
    top_k=10,
    target_class_order=["not_relevant", "relevant", "highly_relevant"]
)

Configuration for imbalanced datasets:

imbalanced_params = ModelTaskParams(
    binary_classification_threshold=0.3,
    class_weights=[0.1, 0.9],
    weighted_ref_histograms=True
)

Attributes

Threshold for labels

Order of target classes

Query/session id column for ranking models

Top k results to consider when computing ranking metrics

Weight of each classes

Whether baseline histograms must be weighted or not while drift metrics

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

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Examples

Attributes