Assess field-level accuracy for multi-output models

Evaluate the performance of your model by field.

始める前に

Role required: admin

このタスクについて

After you have created and tested your model, assess its performance to evaluate how well it's predicting what you want it to predict. For multi-output models that predict multiple fields, you may want to assess the performance of each field individually.

If you have not already created a model, see either Create a model to predict record fields or Create a model to predict incident fields to make one.

Your model must be in Monitoring mode. You can set Monitoring mode on the Assess Your Model screen. For more details, see the Set your preferences step in Create a case field prediction model or Create a model to predict incident fields model.

手順

1. Navigate to the Predictor Results (ml_predictor_results_task) by entering ml_predictor_results_task.list in the Application navigator.
2. Filter the Predicted Output Value Name for the name of the field you want to assess, such as "product" or "category."
3. Group the list by Predicted Correctly by selecting the list controls icon in the top left corner of the screen.
   
4. Divide the number of records where Predicted Correctly is true by the total number of records.
   This represents your accuracy for the specific field.
5. Follow steps 2-4 for each field in which you would like to calculate the accuracy.
6. オプション: View the performance of your model on the Monitoring screen.
   1. Navigate to Task Intelligence for Customer Service > Monitoring in the Application navigator.

      The Monitoring screen allows you to select the model, field, and date range to display in the charts. Accuracy is measured based on whether the top autofilled value or the top 3 recommendations are correct.

      

   2. Select a model.
   3. Select an output column for the field.
   4. Select a date range.

次のタスク

If the accuracy of each field is acceptable, transition your model from monitoring mode to real-time predictions and deploy. If the accuracy of a specific field is not acceptable, you can remove that output field from your model, retrain, and deploy. See Edit a Task Intelligence model for more information on editing your model.