# ModelSpec

Defines how model columns are categorized and used along with model task configuration.

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:

```python
spec = ModelSpec(
    inputs=["age", "income", "credit_score"],
    outputs=["prediction", "probability"],
    targets=["approved"],
    metadata=["customer_id", "timestamp"]
)
```

Creating a spec with custom features:

```python
from fiddler.schemas.custom_features import Multivariate, TextEmbedding

spec = ModelSpec(
    inputs=["user_clicks", "session_time", "review_text_embedding"],
    outputs=["recommendation_score"],
    targets=["user_rating"],
    metadata=["user_id", "session_id"],
    custom_features=[
        Multivariate(
            name="user_behavior",
            columns=["user_clicks", "session_time"],
            n_clusters=5
        ),
        TextEmbedding(
            name="review_clusters",
            column="review_text_embedding",
            source_column="review_text",
            n_clusters=8
        )
    ]
)
```

Creating a spec for ranking models:

```python
ranking_spec = ModelSpec(
    inputs=["query_features", "doc_features", "relevance_score"],
    outputs=["ranking_score"],
    targets=["click_through"],
    decisions=["final_ranking"],
    metadata=["query_id", "doc_id"]
)
```

## schema\_version *: int*

Schema version

## inputs *: List\[str]*

Feature columns

## outputs *: List\[str]*

Prediction columns

## targets *: List\[str]*

Label columns

## decisions *: List\[str]*

Decisions columns

## metadata *: List\[str]*

Metadata columns

## custom\_features *: List\[Multivariate | VectorFeature | TextEmbedding | ImageEmbedding | Enrichment]*

Custom feature definitions

## remove\_column()

Remove a column name from spec if it exists.

**Return type:** None


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