---
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


---

# Model Explainability

# Model Explainability {#ariaid-title1}

* Versão de lançamento: Australia
* 
* Atualizado 12 de mar. de 2026
* 
* ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 min. de leitura

Analyze the importance of each input field to your model's predictions using model explainability. Create a Workflow Classification model that includes a graphical analysis of feature importance by executing the provided
script.

## Antes de Iniciar

* This method uses the Workflow Classification Solution API, instead of the Solution Definition form, to create and train a model with explainability added. For information about the components of Workflow Classification models, see [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.").
* Role required: ml_admin or admin

## Por Que e Quando Desempenhar Esta Tarefa

Model
explainability helps identify the key features that influence the model's predictions during training.  
Nota:  
Explainability can't be added to an existing model. This method uses a script to create and train a new Workflow Classification model. For more information about scripting the creation of Classification solutions, see [ClassificationSolution - Global](https://www.servicenow.com/docs/access?context=ClassificationSolutionAPI&version=australia&pubname=australia-api-reference&ft:locale=en-US).

The script provided in the procedure creates and trains a model with explainability set to true. On the new model's solution form, an additional tab labeled Feature Importance appears. This tab offers a graph of
the relative contribution of each input to the prediction.

## Procedimento

1. Navigate to AllSystem DefinitionScripts - Background.
2. Edit the query filter and table, field, and variable values in the following script according to your planned model, then execute the script.  

       // Define a dataset
                           var myIncidentData = new sn_ml.DatasetDefinition({
                           'tableName': 'incident',
                           'fieldNames': ['category', 'short_description', 'sys_updated_by', 'assignment_group', 'description', 'priority'],
                           'encodedQuery': 'activeANYTHING'
                           });
                           
                           // Define a classification solution definition with explainability field
                           var mySolution = new sn_ml.ClassificationSolution({
                           'label': 'model explainability',
                           'dataset': myIncidentData,
                           'predictedFieldName': 'category',
                           'inputFieldNames': ['short_description', 'priority'],
                           //setting the explainability field to true.
                           'explainability': true,
                           });
                           
                           // Add solution to ClassificationSolutionStore
                           var my_unique_name = sn_ml.ClassificationSolutionStore.add(mySolution)
                           
                           // Submit training job
                           var solutionVersion = mySolution.submitTrainingJob();
                       
   Nota:  
   Substitute the query filter and table, field, and variable names in this script with your own values.
3. Navigate to the ML Solutions \[ml_solution\] table and open your new solution by selecting the value of its Active field.
4. On the solution form, locate and open the Feature Importance tab.  
   Feature Importance displays a drop-down list.

   * The label for this drop-down list is the name of your output (predicted) field, so the label is specific to each model.
   * The values in the drop-down list are the possible output classes for your output field, plus the Global option.
   {#predictive-intel-explainability__ul_p4z_dzp_zdc}

   | List option | Description |
   | Global | Provides an overview of how the model behaves across all predictions, showing the overall impact of each input feature. Select Global to open a graph of the importance of your input fields to predictions for all output classes as a whole. |
   | <kbd class="ph userinput">Your output class value</kbd> | Focuses on the model's behavior for the chosen class only, showing how input features contribute to predictions on a per-class basis. Select one of the possible output classes to open a graph of the importance of your input fields to predictions for that class. |
   |-|-|

   {#predictive-intel-explainability__choicetable_afh_pny_12c}
5. Launch the graphical analysis by selecting a value from the drop-down list.  
   The y axis shows your input fields and the x axis shows the numerical importance for each input. The label of the graph reflects the class that you chose in the drop-down list.  
   You can hover over a bar to display the numerical percentage for each input's importance.

## Resultado

A positive importance value means that the input field increases the model's prediction score. A negative value means that the input field decreases the prediction score.

## O que Fazer Depois

Consider dropping input fields with low importance scores. Retrain your model after modification.
**Conceitos relacionados**   

* [Using Machine Learning APIs](https://servicenow-prod.fluidtopics.net/ZXSzC15BCTxiFn3ZVC_LiQ#using-ml-apis "Use ServiceNow Machine Learning (ML) APIs to train Machine Learning models and run inferences.")  
**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.")  
**Tópicos relacionados**   

* [ClassificationSolution - Global](https://www.servicenow.com/docs/access?context=ClassificationSolutionAPI&version=australia&pubname=australia-api-reference&ft:locale=en-US)