Skip to main content
ModelSpec provides a comprehensive specification of how different columns in your model’s data should be interpreted and used. It categorizes columns into inputs, outputs, targets, decisions, and metadata, and allows for custom feature definitions that enhance model monitoring and analysis capabilities. This specification is crucial for Fiddler to understand your model’s structure, enabling proper monitoring, drift detection, bias analysis, and explainability features. It acts as the contract between your model and Fiddler’s monitoring infrastructure.
  • custom_features (List [Multivariate | VectorFeature | TextEmbedding | ImageEmbedding | Enrichment ])

Examples

Creating a basic model spec for classification:
Creating a spec with custom features:
Creating a spec for ranking models:

Attributes

Schema version
Feature columns
Prediction columns
Label columns
Decisions columns
Metadata columns
Custom feature definitions

remove_column()

Remove a column name from spec if it exists.