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

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ft:locale :

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ft:publication_title :

    - Australia Enable AI

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# Configure TF-IDF for solutions

# Configure TF-IDF for 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 Term Frequency--Inverse Document Frequency (TF-IDF) encoding to classification, clustering, or similarity solutions for Predictive Intelligence.

## Antes de Iniciar

Nota:  
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. For more information, see <https://www.servicenow.com/community/intelligence-ml-articles/dive-deeper-with-clustering-advanced-parameters/ta-p/2695847>.

* Create a classification, clustering, or similarity solution definition or use an existing one.
* Role required: admin or ml_admin
{#configure-tf-idf-classification-similarity__ul_wxv_kxs_1kb}

## Por Que e Quando Desempenhar Esta Tarefa

Predictive Intelligence uses paragraph vector word embedding by default in its classification and similarity frameworks, which is highly effective for processing data comprised of primarily human-readable content.
However, TF-IDF might return better prediction results for records that have machine-generated content, such as alerts and error messages for log files. Choose advanced settings that are appropriate for the kind of data your
solution is processing.  
Nota:  
The steps for configuring TF-IDF are the same for all model frameworks, but TF-IDF support for clustering solution definitions is applicable only if you have a Professional subscription.

## Procedimento

1. Navigate to a Solution Definition, such as AllPredictive IntelligenceSimilaritySolution Definitions.
2. Open a solution definition form.  
   In this example scenario, you use a CMDB similarity definition form.  
3. On the Advanced Solution Settings tab in the Related Links section of the form, click New.  
4. Create a parameter record.
   1. In the Solution Parameters field, click the search icon.
   2. In the ML Solution Parameters screen, select Use tf-idf to generate vectors.

   {#configure-tf-idf-classification-similarity__substeps_ow5_5tj_zjb}  
5. Click Submit.  
   The Advanced Solution Setting record screen refreshes.  
6. Click Submit.  
   Result: TF-IDF is configured for your similarity
   solution. Its solution parameter appears on the Advanced Solution Settings
   tab of your similarity 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 similarity solution](https://servicenow-prod.fluidtopics.net/Kt1hVY60v81Hn8qbJIBEpw "Create and train a machine learning solution to collect and compare your existing records to new similar records. For example, you can compare the text in an open Incident record to a resolved Incident record to reuse its resolution.")
* [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.")
* [Create and train a clustering solution](https://servicenow-prod.fluidtopics.net/ecYIQ6D0OLyg_ddAGlsYCA "Group similar records into clusters so you can address them collectively or identify patterns.")