---
sourceDocument: Australia Enable AI
sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/pt-BR/intelligent-experiences

 Release :

    - australia

ft:locale :

    - pt-BR

ft:publication_title :

    - Australia Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Configure XGBoost for classification or regression solutions

# Configure XGBoost for classification or regression solutions {#ariaid-title1}

* Versão de lançamento: Australia
* 
* Atualizado 12 de mar. de 2026
* 
* ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 1 min. de leitura

Apply XGBoost encoding to optimize the training for your classification or regression solutions.

## Antes de Iniciar

* Create a classification solution definition or use an existing one.
* Create a regression solution definition or use an existing one.
* Role required: admin or ml_admin
{#configure-xgboost-classification-regression-solutions__ul_bbp_gcr_3lb}

## Por Que e Quando Desempenhar Esta Tarefa

XGBoost is an optional gradient boosting framework that uses multiple decision trees and supports both Paragraph Vector-based text and TF-IDF distance-based text.
LogR is the default distance-based model algorithm.

Configuring advanced settings on your ML solutions is optional. If you choose to configure any of these settings, make sure you're well informed regarding the technology you're enabling in the solution, and that you have a
use case that benefits from what the technology offers.  
In this example scenario, you apply XGBoost to both a classification solution and a regression solution.  
Nota:  
The regression framework is deprecated in the Australia release. You can continue to use existing regression solutions but you can't create new ones.

## Procedimento

1. Navigate to AllPredictive IntelligenceClassificationSolution Definitions.
2. Open a classification solution definition form.
3. On the Advanced Solution Settings tab in the Related Links section of the form, select New.  
4. Create a parameter record.
   1. In the Solution Parameters field, select the search icon.
   2. In the ML Solution Parameters screen, select Use XGBoost algo for classification model training.

   {#configure-xgboost-classification-regression-solutions__substeps_ow5_5tj_zjb}  
5. Select Submit.  
   The Advanced Solution Setting record screen refreshes.  
6. Select Submit.  
   Result: XGBoost is configured for your classification solution. Its solution parameter appears on the Advanced Solution Settings tab of your classification definition form.  
   Nota:  
   Follow the steps below if you want to configure XGBoost on a regression solution.
7. Navigate to Predictive IntelligenceRegressionSolution Definitions (available only if you had created a regression solution before the regression framework was deprecated).
8. In this second scenario, you open a regression solution definition form.
9. Repeat the steps from the previous classification solution example, except this time you're using a regression solution.
10. Select Submit.  
    Result:

    XGBoost is configured for your regression solution. Its solution parameter appears on the Advanced Solution Settings tab of your regression solution definition form.
**Tarefas relacionadas**   

* [Create and train a classification solution](https://servicenow-prod.fluidtopics.net/xLGbLdMK5yW5QC6GrOAZBA "Specify the records used to train a classification solution, what fields trigger a prediction, and how often you want to retrain your solution.")
* [Create and train a regression solution](https://servicenow-prod.fluidtopics.net/2TF6VH3~sw~lEXoKgUFSxg "Train your solution by using historical data to predict numeric outputs, such as a temperature or a stock price. For example, you can use regression to estimate the time it takes to resolve an incident or a case.")