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sourceDocument: Yokohama Enable AI
sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/yokohama/intelligent-experiences
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ft:publication_title :
- Yokohama Enable AI
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# Configuring target metrics for a trained classification solution
# Configuring target metrics for a trained classification solution {#ariaid-title1}
Release version: Yokohama
Updated January 30, 2025
 4 minutes to read
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## Summary of Configuring target metrics for a trained classification solution
This guide explains how ServiceNow customers can configure key classification metrics---precision, coverage, and recall---for a trained machine learning (ML) classification solution using the Solution Statistics tab.
Setting these metrics at the solution level helps optimize model performance across all classes and supports tailoring the solution to specific business needs.
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## Configuring Solution-Level Metrics
ServiceNow Predictive Intelligence offers three classification metrics:
* **Precision:** Accuracy of the predictions made.
* **Coverage:** Percentage of records the model makes predictions for.
* **Recall:** Ability to identify all relevant cases.
When you adjust one metric's target value, the other two metrics will automatically change due to their interdependent nature. This iterative adjustment allows you to balance these metrics based on your specific requirements.
To configure metrics at the solution level:
* Go to the Solution Statistics tab of a trained ML solution.
* Review the instructional banners explaining each metric.
* Select the desired metric from the Target Metric dropdown.
* Enter a target percentile value between 0 and 100.
* Click **Apply Values** to update the solution's metrics.
The system will then recalculate and display estimated values for precision, recall, and coverage at both the solution and class levels, automatically adjusting class-level settings to reflect solution-level targets.
## Practical Examples of Metric Configuration
* **Precision-focused:** For scenarios like automated incident routing where accuracy is critical, setting precision to 80% ensures the system predicts correctly at least 80% of the time.
* **Coverage-focused:** When the goal is to classify a high volume of incidents quickly, setting coverage to 70% increases the number of classified records but may reduce precision.
* **Recall-focused:** In security use cases such as phishing email detection, setting recall high (e.g., 95%) prioritizes catching all relevant cases, even if precision decreases.
These examples illustrate how adjusting one metric affects the others and highlight the importance of aligning metric settings with business priorities and data characteristics.
## Class-Level Insights and Iterative Refinement
After applying solution-level metric targets, you can review detailed class-level results to identify which classes have the highest or lowest precision, coverage, and recall. Sorting these results helps you understand where the model performs well and where adjustments might be needed.
By iteratively modifying metric values and reviewing outcomes, you can fine-tune the classification solution to meet your organization's accuracy, coverage, and recall goals efficiently.
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 which 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.
If you need to adjust these default values for a use case, refer to the sample configurations
below. 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 when you
change the target metric value for one metric, such as precision, it 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.