Train the classification solution to predict the subcategory for Ethics Complaint
Train the legal request classification solution definition to identify and predict an subcategory using the description for an Ethics Complaint.
Before you begin
Role required: sn_lg_pi.admin and platlform_ml_read
Ensure that you have installed Predictive Intelligence (com.glide.platform_ml) and Predictive Intelligence for Legal Service Delivery (sn_lg_pi) plugins.
Note: A minimum of ten thousand records are needed to train the classifications.
Procedure
-
Navigate to Predictive Intelligence > Classification > Solution Definitions.
-
In the Classification Definitions list, search for and select the Ethics Complaints Sub-categorization (ml_x_snc_sn_lg_pi_global_ethics_complaints_sub_categorization) solution.
-
On the form, verify the default field values and customize the solution as required.
For more information about the Classification Definition form fields, see Create and train classification solution.
Parent Topic:Configure Predictive Intelligence for Legal Service Delivery
---
title: Train the classification solution to predict the subcategory for Ethics Complaint
description: Train the legal request classification solution definition to identify and predict an subcategory using the description for an Ethics Complaint.
locale: en-US
canonical_url: https://www.servicenow.com/docs/r/australia/employee-service-management/legal-service-delivery/lsd-PI-train-auto-categry-ethics.html
release: australia
product: Legal Service Delivery
classification: legal-service-delivery
topic_type: task
last_updated: "2026-09-10"
reading_time_minutes: 1
breadcrumb: [Configure, Predictive intelligence for Legal Service Delivery, Integration with ServiceNow applications, Legal Service Delivery, Legal and Contract Operations, Employee Service Management]
---
# Train the classification solution to predict the subcategory for Ethics Complaint
Train the legal request classification solution definition to identify and predict an subcategory using the description for an Ethics Complaint.
## Before you begin
Role required: sn\_lg\_pi.admin and platlform\_ml\_read
Ensure that you have installed Predictive Intelligence \(com.glide.platform\_ml\) and Predictive Intelligence for Legal Service Delivery \(sn\_lg\_pi\) plugins.
**Note:** A minimum of ten thousand records are needed to train the classifications.
## Procedure
1. Navigate to **Predictive Intelligence** > **Classification** > **Solution Definitions**.
2. In the Classification Definitions list, search for and select the Ethics Complaints Sub-categorization \(ml\_x\_snc\_sn\_lg\_pi\_global\_ethics\_complaints\_sub\_categorization\) solution.
3. On the form, verify the default field values and customize the solution as required.
For more information about the Classification Definition form fields, see [Create and train classification solution](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/intelligent-experiences/create-solution-definition.md).
**Parent Topic:**[Configure Predictive Intelligence for Legal Service Delivery](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/employee-service-management/legal-service-delivery/lsd-PI-configure-landing.md)
| title | Train the classification solution to predict the subcategory for Ethics Complaint |
| description | Train the legal request classification solution definition to identify and predict an subcategory using the description for an Ethics Complaint. |
| locale | en-US |
| canonical_url | https://www.servicenow.com/docs/r/australia/employee-service-management/legal-service-delivery/lsd-PI-train-auto-categry-ethics.html |
| release | australia |
| product | Legal Service Delivery |
| classification | legal-service-delivery |
| topic_type | task |
| last_updated | 2026-09-10 |
| reading_time_minutes | 1 |
| breadcrumb | ConfigurePredictive intelligence for Legal Service DeliveryIntegration with ServiceNow applicationsLegal Service DeliveryLegal and Contract OperationsEmployee Service Management |