---
sourceDocument: Australia Enable AI
sourceDocumentLink: https://www.servicenow.com/docs/r/australia/intelligent-experiences
Release :
- australia
ft:locale :
- en-US
ft:publication_title :
- Australia Enable AI
ft:clusterId :
- platai
bundleId :
- platai
workflow :
- Platform
---
# Configuring target metrics for a trained classification solution
# Configuring target metrics for a trained classification solution {#ariaid-title1}
Release version: Australia
Updated August 6, 2026
 4 minutes to read
Summarize
 Summarized using AI
This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.
## Summary of Configuring target metrics for a trained classification solution
ServiceNow's Predictive Intelligence enables customers to configure key classification metrics---precision, coverage, and recall---at the solution level for trained machine learning classification models.
These metrics help you tailor model predictions to meet specific business needs, such as improving accuracy or ensuring a minimum percentage of cases are classified.
Adjusting one metric affects the others, allowing iterative tuning to find the optimal balance for your use case.
Show full answer Show less
## Configuring Target Metrics
To configure target metrics for a trained classification solution:
* Go to the Solution Statistics tab of the trained ML solution form.
* Review on-screen guidance that explains each metric at both the solution and class levels.
* Select the metric you want to adjust (precision, coverage, or recall) from the Target Metric dropdown.
* Enter a numeric percentile between 0 and 100 in the Target Metric Value field.
* Click **Apply Values** to update the metrics. The system recalculates the metric values based on your target and automatically adjusts related metrics and class-level values.
This approach helps you set meaningful targets while accommodating data distribution and model limitations.
## Practical Examples and Considerations
* **Precision-focused use case:** For automated incident routing, setting precision to 80% ensures predicted assignments are correct at least 80% of the time. The system may slightly exceed this target, indicating successful tuning.
* **Coverage-focused use case:** When aiming to classify at least 70% of incidents, increasing coverage may reduce precision, reflecting a trade-off where more cases are classified but with less accuracy.
* **Recall-focused use case:** In security scenarios such as phishing email detection, prioritizing recall (e.g., setting to 95%) reduces the chance of missing threats but may lower precision and coverage, reflecting acceptable trade-offs for critical detections.
## Class-Level Impact
Metric settings applied at the solution level propagate to individual classes, allowing you to monitor precision, coverage, and recall per class. Sorting by estimated precision helps identify classes with the highest accuracy and guides further tuning.
## Benefits for ServiceNow Customers
* Fine-tune your ML classification models to align with specific business requirements.
* Understand and balance trade-offs between precision, coverage, and recall to optimize model performance.
* Leverage automated recalculations and class-level insights to iteratively improve the solution.
* Apply targeted metrics without manually configuring each class, saving time and reducing complexity.
Set values for precision, coverage, and recall statistics for a trained machine learning
solution.
## Setting classification metric values at the class or solution level {#configuring-target-metrics-trained-classification-solution__section_p1j_wsk_xjb}
Predictive Intelligence provides three classification
metric types: precision, coverage, and recall. You configure these metrics on the Solution
Statistics tab of a trained classification solution form. While you can manually set values to
these metrics at the class level, doing so can be challenging if you have a large number of
classes to cover. In many cases, you may not know the best value to set until your solution is
trained. This topic focuses on setting the metric values at just the solution level.
## Configuring solution metrics {#configuring-target-metrics-trained-classification-solution__section_apz_wsk_xjb}
When you apply a value to one metric, it changes the values of the other two. This behavior
enables you to modify your metrics iteratively in real time to see which value combinations
render particular results. When you apply a new value to a metric, the system recomputes it by
considering its new targets.
Applying a value to a metric asks the system to train its predictions to favor the metric you set
based on the highest percentage value, and at a cost to the other metrics. The system tries to meet these values but may not set them exactly as you request due to how the data you're training is distributed.
When you apply metric values at the solution level, the system automatically sets the
appropriate values at the class level.
Here are the basic steps for configuring a target metric for your solution.
1. Navigate to the Solution Statistics tab of a trained ML solution.
2. Review the messages on the green banners of the screen. These define each of the metrics so you can better understand the values you want to assign to the solution. The first two message banners address estimated solution-level metrics. The third banner addresses class-level results based on the solution values you applied.
3. In the Target Metric choice list, select the metric you want to configure.
4. In the Target Metric Value field, enter a numeric percentile value between 0-100.
5. Click Apply Values.
6. Result: On the Solutions Statistics tab, you can review the change in values to the Estimated Solution Precision, Estimated Solution Recall, and Estimated Solution Coverage. The system calculates these values based on the Target Metric you select and the Target Metric Value you enter for the solution.
{#configuring-target-metrics-trained-classification-solution__ol_tb5_51s_xjb}
Here's a sample landing page for a recently trained classification solution. As you can see,
the precision metric is 44.18, recall is 41.26, and coverage is 77.23.
To adjust these default values for a use case refer to the sample configurations as follows. For example, based on the classification solution you're implementing, you might want to change the target metric value for precision,
recall, or coverage. Keep in mind that changing the target metric value for one metric (such as precision) impacts the values of the recall and coverage metrics as well.
## Precision configuration example {#configuring-target-metrics-trained-classification-solution__section_nbh_xsk_xjb}
In this example scenario, you're replacing a manual triage process for routing incident records with
an ML classification solution that automatically assigns the records to the correct assignment group. For this scenario, you have a target value in mind and the system must predict correctly at least 80% of the time. So you set
the precision metric value to 80 and click Apply Values.
Here are the metric values the system applied to the solution. In this scenario, the precision
value of 80.04 slightly exceeded your request for 80%, so you're likely satisfied with that
value.
## Coverage configuration example {#configuring-target-metrics-trained-classification-solution__section_x1v_1jg_yjb}
In another example scenario where you're replacing a manual triage process for routing incident
records, your minimum goal is to predict at least 70% of incoming incidents in the first quarter of the year. So you set the coverage metric value to 70 and click Apply Values.
The metric values the system applied to the solution are shown in the following image. The
coverage metric value increased from 35.99 to 55.98. However, the precision metric decreased
from 80.18 to 64.97. This could be because you set the coverage metric to a relatively high
value of 70, or perhaps because of how the data you're training is distributed.
## Recall configuration example {#configuring-target-metrics-trained-classification-solution__section_jwb_d3g_yjb}
In another scenario, classifying if an incoming email is a Phish or not can be an important
use case in a security-related machine learning solution. In this situation, it's very important
to identify every Phish, and it may be okay to report a non-Phish as a Phish occasionally.
However, no real Phish should be classified as a non-Phish. In such situations, the recall
metric must have a high value, which might lead to lower percentages for precision and coverage.
So here you can set the recall metric to 95 and click Apply Values.
Here are the metric values the system applied to the solution. The recall metric value
increased from 54.87 to 61.03. However, the precision metric decreased from 60.1 to 55.44. This
is likely because you set the recall metric to the high value of 95.
## Class-level results for the solution metric values you apply to your solution {#configuring-target-metrics-trained-classification-solution__section_djw_rjg_yjb}
The following image shows an example of the class-level results the system applied to a
solution's precision, coverage, and recall statistics for 37 classes. You can keep modifying the
metric values until you're fully satisfied with the results.
By Sorting (z to a) on the Estimated Precision column you can see which classes have the
highest precision for the solution.