# ModelTaskParams

Configuration parameters for different model task types and evaluation metrics.

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:

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

Configuration for multi-class classification with class weights:

```python
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:

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

Configuration for imbalanced datasets:

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

## binary\_classification\_threshold *: float | None*

Threshold for labels

## target\_class\_order *: List | None*

Order of target classes

## group\_by *: str | None*

Query/session id column for ranking models

## top\_k *: int | None*

Top k results to consider when computing ranking metrics

## class\_weights *: List\[float] | None*

Weight of each classes

## weighted\_ref\_histograms *: bool | None*

Whether baseline histograms must be weighted or not while drift metrics


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