> For the complete documentation index, see [llms.txt](https://docs-old.evidentlyai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs-old.evidentlyai.com/v0.1.57/user-guide/customization/options-for-quality-metrics.md).

# Options for Quality Metrics

**An example of setting custom options in Data Drift and Probabilistic Classification Performance reports on Wine Quality Dataset:**

{% embed url="<https://colab.research.google.com/drive/1W7l3iAILkMti-3qcBLrU5JrW24lSOMR3>" %}

## Available Options

These options apply to different plots in the Evidently reports: Data Drift, Categorical Target Drift, Numerical Target Drift, Classification Performance, Probabilistic classification performance.

You can specify the following parameters:

* **conf\_interval\_n\_sigmas**: *int* Default = 1.
  * Defines the width of confidence interval depicted on plots. Confidence level indicated in sigmas (standard deviation).
  * Works to the feature or target distribution plots in the Data Drift and Numerical Target Drift reports.
* **classification\_threshold**: *float.* Default = 0.5.
  * Defines classification threshold for binary probabilistic classification.
  * Works to the Probabilistic Classification report.
* **cut\_quantile**: *tuple\[str, float]* or *dict\[str, tuple\[str, float].* Default = None.
  * Cut the data above the given quantile from the histogram plot if side parameter == *'right'*.
  * Cut the data below the given quantile from the histogram plot if side parameter == *'left'*.
  * Cut the data below the given quantile and above *1 - the given quantile* from the histogram plot if side parameter == *'two-sided'*.
  * Data used for metric calculation doesn't change.
  * Applies to all features (if passed as *tuple*) or certain features (if passed as *dictionary*).
  * Works to the Categorical Target Drift, Probabilistic Classification and Classification reports, and affects tables with Target/Prediction behavior by feature, and Classification Quality by Feature.

### How to define Quality Metrics Options

1\. Define a **QualityMetricsOptions** object.

```python
options = QualityMetricsOptions(
                           conf_interval_n_sigmas=3, 
                           classification_threshold=0.8, 
                           cut_quantile={'feature_1': ('left': 0.01), 'feature_2': 0.95, 'feature_3': 'two-sided': 0.05})
```

2\. Pass it to the **Dashboard** class:

```
dashboard = Dashboard(tabs=[DataDriftTab(), ProbClassificationPerformanceTab()], 
options=[options])
```