Interacting with Monte Carlo's Detectors

Tools that help you influence Monte Carlo's Machine Learning detections

Monte Carlo offers three main customization tools to control, tune, and influence our automatic ML detectors

  • Sensitivity Settings
  • Share Feedback
  • Exclusion Windows
ToolWhat is it used for?
Sensitivity Settings- Adjust thresholds to result in more or fewer anomalies
- Set the interest/criticality of a table
- Reduce alert fatigue
Share Feedback- Improve the model by effecting training
- Lower the thresholds after an incident
Exclusion Windows- Downtime, Corrupted data
- Collection issues
- Suppress alerts during holidays

Sensitivity Settings

Monte Carlo allows you to change the sensitivity of our models to increase or decrease the likelihood of generating an incident based on anomalous behavior. High sensitivity will generate more incidents and is best for your most important tables. Low sensitivity will generate fewer incidents and is best for less important tables or tables that have generated historical noisy incidents.

See for more information.

Share Feedback

Our machine learning (ML) models use your feedback to learn and improve. We are continually working to enhance these models, and customer feedback assists us in making further improvements.

See for more information.

Exclusion Windows

When you have a service provider outage or an integration issue between Monte Carlo and your data sources, you might have an undesirable gap in detection that causes incorrect detection or reduces the sensitivity of Monte Carlo's anomaly detection.

While Monte Carlo's ML models know how to handle weird data patterns which can arise to some extent there is the known ML saying β€œgarbage in, garbage out” and we are aware that sometimes issues occur, either on our side or yours.

See and go to Settings > Filters > Exclusion Windows in the app to get started.