Once a monitor triggers, alerting you that performance has dropped, troubleshoot your performance monitors in the Performance Tracing tab within your model. Performance tracing enables you to easily understand the features and slices that impact your model's performance the most and begin resolution.
The Performance Tracing tab immediately visualizes your performance metric over time layered on top of your model's prediction volume. This gives you a broad understanding of your model's overall performance to identify areas of improvement, compare different datasets, and examine problematic slices.
The 'Performance Over Time' graph is highly configurable. Use this graph to visualize different dimensions such as:
- Environments: pick from production, validation, or training environments
- Versions: pick from any model version
- Time periods: zoom in or out on any time period for your dataset
- Performance metrics: choose from an array of performance metrics such as accuracy, AUC, MAE, MAPE, RMSE, sMAPE, WAPE, and more.
- Filters: layer additional filters across features, prediction values, actuals, and tags to see your model's performance on a more granular level.
Send data from different environments to compare model performance between training, validation, or a different time period within your production data. Comparing your production data helps you identify gaps in data quality or where drift occurs for simple troubleshooting.
Navigate to the toolbar, click 'Add a Comparison' and pick from a different environment, version, or time period.
To identify key areas to improve with your comparison dataset, break performance issues down using Performance Insights and our Performance Heat Map.
The Performance Insights panel surfaces the worst-performing slices impacting your model to perform a counterfactual analysis. Use Performance Insights to exclude features or slices as a filter to identify how your model's performance changes.
To do this, scroll down to the 'Performance Insights' card and click on a feature. Once you click into a feature, a histogram of your feature slices will populate on the left side with options to 'Add cohort as a filter', 'Exclude cohort as a filter', and 'View explainability'.
The performance heat map visualizes your feature's performance by slice view to visually indicate the worst-performing slices within each feature. Click on the carrot on the left side of your feature's name to uncover its histogram.
Compare feature performance amongst different environments, versions, and filters to uncover areas of improvement. Look out for different colors and distributions between the two histograms to identify areas of missing or poor-performing data.
Once you've identified an area of interest, click on the 'View Feature Details' link to uncover a detailed view of your feature distribution over time.
Scrolling below the 'Performance Breakdown' card, you'll see a Calibration Chart. This chart plots Average Actuals against Estimated Probability. The better calibrated the model, the closer the plotted points will be to the diagonal line.
- If the model points are below the line: your model has over-forecast in its prediction. For example, predicting a credit card charge has a high probability of fraud when it not fraudulent.
- If the model points are above the line: your model has an under-forecast in its prediction. For example, predicting a credit card charge has a low likelihood of being fraud when it's actually fraudulent.