> ## Documentation Index
> Fetch the complete documentation index at: https://docs.evidentlyai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Classification

> Overview of the Classification Quality Preset

**Pre-requisites**:

* You know how to use [Data Definition ](/docs/library/data_definition)to prepare the data.
* You know how to create [Reports](/docs/library/report).

**Report.** To run a Preset on your data for a single current dataset:

```python theme={null}
report = Report([
    ClassificationPreset(),
])

my_eval = report.run(current, None)
```

**Test Suite**. To add pass/fail classification quality Tests, auto-generated from the `ref` dataset:

```python theme={null}
report = Report([
    ClassificationPreset(),
],
include_tests=True)

my_eval = report.run(current, ref)
```

# Overview

The `ClassificationPreset` allows you to evaluate and visualize the performance on classification tasks, whether binary or multi-class. You can run this Report either for a single dataset or compare it against a reference dataset (such as past performance, or a different model/prompt).

<img src="https://mintcdn.com/evi/DLHmQMW9F8KXZznS/images/metrics/preset_classification-min.png?fit=max&auto=format&n=DLHmQMW9F8KXZznS&q=85&s=dccadeeb86883e3f46ab8457b03e0a71" alt="" width="2072" height="1276" data-path="images/metrics/preset_classification-min.png" />

* **Various metrics**: Accuracy, Precision, Recall, F1-score, ROC AUC, LogLoss, etc.

* **Various visualizations**: Class Representation, Confusion Matrix, Class Separation Quality, Probability Distribution, ROC Curve, PR Curve, etc.

<img src="https://mintcdn.com/evi/DLHmQMW9F8KXZznS/images/metrics/preset_classification_2-min.png?fit=max&auto=format&n=DLHmQMW9F8KXZznS&q=85&s=4f0ed325875ce2740721f0f554a2f883" alt="" width="2092" height="1020" data-path="images/metrics/preset_classification_2-min.png" />

Additionally, if you include feature columns, the Report will show Classification Quality by column. It displays the relationship between columns/features and the target, showing how the system performs on different data segments.

**Test Suite**. If you enable Tests, this will automatically run checks to assess if the model performance metrics are within bounds.

<img src="https://mintcdn.com/evi/DLHmQMW9F8KXZznS/images/metrics/test_preset_classification-min.png?fit=max&auto=format&n=DLHmQMW9F8KXZznS&q=85&s=160c7d0bdfdc0007fc80eead4ed3c3d7" alt="" width="2230" height="1232" data-path="images/metrics/test_preset_classification-min.png" />

Tests are auto-generated:

* **Based on reference dataset.** If the reference dataset is provided, conditions like expected prediction accuracy will be derived from it.

* **Based on heuristics.** If there is no reference, Evidently will create a dummy classification model as a baseline and run checks against it.

<Info>
  **How Tests work.** Read about [Tests](/docs/library/tests) and check defaults for each Test in the [reference table.](/metrics/all_metrics)
</Info>

## Use case

These Presets are useful in various scenarios:

* **Model / system comparison**. Compare predictive system performance across different datasets, such as during A/B testing, when experimenting with different prompt versions and configurations, etc.

* **Production monitoring**. You can run evaluations whenever you get true labels in production. Use this to communicate and visualize performance, decide on model updates / retraining, etc.

* **Debugging**. If you notice a drop in performance, use the visual Report
  Model Monitoring: Track the performance of a classification model over time to diagnose quality issues, explore the model errors and underperforming segments.

## Data requirements

* **Target and prediction columns**. Required to calculate performance.

* **One or two datasets**. Pass two for a side-by-side comparison or to auto-generate tests.

* (Optional) **Input features.** Include if you want to explore column-target relations.

* (Optional) **Timestamp**. If available, pass it to appear on some plots.

<Info>
  **Data schema mapping.** Use the [data definition](/docs/library/data_definition) to map your data structure.
</Info>

## Report Customization

You can customize the Report in several ways:

* **Change Test conditions**. To modify the auto-generated conditions, you can set yours: either a different condition relative to the reference or any custom conditions.

* **Modify Report composition**. You can add additional metrics, such as column Correlations, Missing Values, or Data Drift. It's often useful to add `ValueDrift("target")`to evaluate if there is a statistical distribution shift in the model target (concept drift).

<Info>
  **Creating a custom Report**. Check the documentation for creating a [custom Report](/docs/library/report) and modifying [Tests](/docs/library/tests) conditions.
</Info>