Monitoring Realized Performance for Multiclass Classification

We currently support the following standard metrics for multiclass classification performance calculation:

• roc_auc

• f1

• precision

• recall

• specificity

• accuracy

• average_precision

For more information about estimating these metrics, refer to the Calculating Standard Performance Metrics for Multiclass Classification section.

We also support the following complex metric for multiclass classification performance calculation:

• confusion_matrix

• business_value: a metric that combines the components of the confusion matrix using user-specified weights for each element, allowing for a connection between model performance and business results.

For more information about calculating these metrics, refer to the Calculating Confusion Matrix Elements for Multiclass Classification and Calculating Business Value for Multiclass Classification sections.

• Calculating Standard Performance Metrics for Multiclass Classification
  • Just The Code
  • Walkthrough
  • Insights
  • What Next
• Calculating Confusion Matrix Elements for Multiclass Classification
  • Just The Code
  • Walkthrough
  • Insights
  • What’s Next
• Calculating Business Value for Multiclass Classification
  • Just The Code
  • Walkthrough
  • Insights
  • What’s Next