Model Performance

The model performance dashboard enables you to analyze your model's performance either over time or between different model versions. This analysis provides insights across various parameters, comparing predicted and actual performance.

Basic concepts:

• Baseline: Users can define the baseline basis on ‘Tag’ or segment of data based on ‘date’.
• ‍Frequency: Users can define how frequently they want to calculate the monitoring metrics
• ‍Alerts frequency: Users can configure how frequently they want to be notified about the alerts

The model performance dashboard enables you to analyze your model's performance either over time or between different model versions. This analysis provides insights across various parameters, comparing predicted and actual performance.

The generated model performance report will display various metrics like accuracy, precision, recall, and quality.

To access the Model performance dashboard through SDK:

project.get_model_performance_dashboard()

Help function to get all parameters and payloads for the Model performance dashboard

help(project.get_model_performance_dashboard)

All the model results are stored in the separate tags

project.all_tags()

With AryaXAI,  proactively identify issues with the performance of your models post-deployment by using 'Monitors'.

Get the Model Performance of 'Active' Model through the AryaXAI SDK - Users can create a performance dashboard based on the predicted values. These are the model output variables: "Predicted_value_AutoML", "Prediction_category_AutoML",  "pred_proba_AutoML"

project.get_model_performance()