# Reports

![](/files/j6jbUl1D3KSETQhKNsUY)

Evidently includes a set of pre-built Reports. Each of them addresses a specific aspect of the data or model performance. You can think of reports as combinations of the metrics and statistical tests that are grouped together.

The calculation results can be available in one of the following formats:

* An interactive visual **Dashboard** displayed inside the Jupyter notebook.
* An **HTML report.** Same as dashboard, but available as a standalone file.
* A **JSON profile.** A summary of the metrics, the results of statistical tests, and simple histograms.
* A live monitoring dashboard, currently available through [integration with Grafana](/v0.1.57/integrations/evidently-and-grafana.md).

## Reports by type

Currently, you can choose between 7 different Report types.

{% content-ref url="/pages/UWrctyvLiuW4MdlFeCpX" %}
[Data Drift](/v0.1.57/reports/data-drift.md)
{% endcontent-ref %}

{% content-ref url="/pages/6cGfuLrjpLhEyhbFZY7m" %}
[Data Quality](/v0.1.57/reports/data-quality.md)
{% endcontent-ref %}

{% content-ref url="/pages/0AXJKr58agMoj5eAP1N9" %}
[Categorical Target Drift](/v0.1.57/reports/categorical-target-drift.md)
{% endcontent-ref %}

{% content-ref url="/pages/kl8y7JYB5qbGop8RkEER" %}
[Numerical Target Drift](/v0.1.57/reports/num-target-drift.md)
{% endcontent-ref %}

{% content-ref url="/pages/6NYcT5PfS2h8kFFkSliG" %}
[Regression Performance](/v0.1.57/reports/reg-performance.md)
{% endcontent-ref %}

{% content-ref url="/pages/D53zwi68OrJfS1P80ra7" %}
[Classification Performance](/v0.1.57/reports/classification-performance.md)
{% endcontent-ref %}

{% content-ref url="/pages/uSzIN4SJu23UDLHf1vj2" %}
[Probabilistic Classification Performance](/v0.1.57/reports/probabilistic-classification-performance.md)
{% endcontent-ref %}

## Show me the code

If you want to see the code, go straight to [examples](/v0.1.57/examples.md).


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