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

# Anomalous column count

> Learn how to use the anomalous column count test for automated anomaly detection across all columns

## Definition

The anomalous column count test automatically learns time series patterns for each
column in your dataset and detects when values fall outside predicted bounds. For
numeric columns, it tracks statistical measures (like averages) over time, while for
categorical columns, it monitors category counts.

The test continuously learns expected ranges for each column and counts how many
columns exhibit anomalous behavior on each evaluation, comparing this count against
your specified threshold.

## Taxonomy

* **Task types**: Tabular classification, tabular regression.
* **Availability**: <Tooltip tip="Monitor a model in production, measure its health, check for drifts and set up alerts.">monitoring</Tooltip> only.

<Warning>
  This test is only available in monitoring mode as it requires historical data
  to learn time series patterns and establish baseline expectations for each
  column.
</Warning>

## Why it matters

* **Automated monitoring**: Provides comprehensive data quality monitoring with minimal configuration required
* **Early anomaly detection**: Identifies unusual patterns across all columns simultaneously before they impact model performance
* **Time series learning**: Adapts to natural variations and trends in your data over time
* **Comprehensive coverage**: Monitors both numeric and categorical columns automatically
* **Minimal setup**: No need to manually configure thresholds for individual columns - the system learns appropriate bounds

## How it works

The test operates through several phases:

1. **Learning phase**: Analyzes historical data to establish time series patterns for each column

   * **Numeric columns**: Tracks statistical measures (averages, medians, etc.) over time
   * **Categorical columns**: Monitors counts of each category over time

2. **Prediction**: Uses learned patterns to predict expected upper and lower bounds for each column's current values

3. **Anomaly detection**: Compares current column values against predicted bounds

   * Values outside the confidence interval are flagged as anomalous

4. **Counting**: Counts the total number of columns exhibiting anomalous behavior

5. **Threshold comparison**: Compares the anomalous column count against your specified threshold

## Configuration parameters

The test supports an optional `interval_width` parameter that controls the confidence interval for anomaly detection:

* **interval\_width**: Confidence interval width (default: 0.95)
  * `0.95` = 95% confidence interval (stricter, detects more anomalies)
  * `0.99` = 99% confidence interval (more lenient, detects fewer anomalies)

## Test configuration examples

If you are writing a `tests.json`, here are a few valid configurations for the anomalous column count test:

<CodeGroup>
  ```json Monitoring - Basic Setup theme={null}
  [
    {
      "name": "No anomalous columns detected",
      "description": "Alerts when any column shows anomalous behavior based on learned patterns",
      "type": "integrity",
      "subtype": "anomalousColumnCount",
      "thresholds": [
        {
          "insightName": "anomalousColumnCount",
          "measurement": "anomalousColumnCount",
          "operator": "<=",
          "value": 0  // No anomalous columns allowed
        }
      ],
      "subpopulationFilters": null,
      "mode": "monitoring",
      "usesProductionData": true,
      "evaluationWindow": 86400, // 24 hours (daily evaluation)
      "delayWindow": 0,
      "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689" // Some unique id
    }
  ]
  ```

  ```json Monitoring - With Custom Confidence Interval theme={null}
  [
    {
      "name": "Anomalous columns with 99% confidence",
      "description": "Uses 99% confidence interval for more lenient anomaly detection",
      "type": "integrity",
      "subtype": "anomalousColumnCount",
      "thresholds": [
        {
          "insightName": "anomalousColumnCount",
          "insightParameters": [
            {
              "name": "interval_width",
              "value": 0.99 // 99% confidence interval (less sensitive)
            }
          ],
          "measurement": "anomalousColumnCount",
          "operator": "<=",
          "value": 0
        }
      ],
      "subpopulationFilters": null,
      "mode": "monitoring",
      "usesProductionData": true,
      "evaluationWindow": 86400, // 24 hours
      "delayWindow": 0,
      "syncId": "96622fba-ea00-4e42-8f42-5e8f5f60805f" // Some unique id
    }
  ]
  ```
</CodeGroup>