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

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    - australia

ft:locale :

    - pt-BR

ft:publication_title :

    - Australia Enable AI

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---

# Config parameters for model config in classification

# Config parameters for model config in classification {#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

Customize the training behavior of your classification solution by passing in a dictionary of parameters in JSON format.

## Antes de Iniciar

Role required: ml_admin or admin

## Por Que e Quando Desempenhar Esta Tarefa

You can fine-tune model performance by passing in a dictionary of parameters to adjust training behavior.

Understand the machine learning hyperparameters relevant to your model objective. This option is advanced and should be modified with care.

You can add these parameters in the solution definition form by using the following procedure.

Alternatively, you can use a script to add these parameters. For the format to use when scripting, see the What to do next section of this page.

## Procedimento

1. Navigate to AllPredictive IntelligenceClassificationSolution Definitions.
2. Open an existing classification solution or create a new one.
3. Select the Advanced Solution Settings tab in the Related Links section of the form.
4. Select New to open the Advanced Solution Setting (ml_advanced_solution_settings) form.
5. In the Solution Parameters field, search for <kbd class="ph userinput">Config parameters for model config in classification</kbd>.
6. In the User Inputs field, enter the following key-value pairs, after editing the values per your requirements.  
   Nota:  
   When pasting the parameters into the solution definition form, don't include JSON's enclosing label, braces, or parentheses. Enter the key-value pairs in the format shown here, after editing the values according to your requirements.

       'max_depth': 10,'learning_rate': 0.1,'objective': 'multi:softprob','num_class': 11,'random_state': 10,'eval_metric': 'mlogloss','booster': 'gbtree','n_estimators': 150,'verbosity': 1,'use_label_encoder': false,'tree_method': 'hist','num_parallel_tree': 1,'gamma': 0.25,'subsample': 0.8,'max_delta_step': 5,'reg_alpha': 0,'reg_lambda': 1,'colsample_bytree': 0.8,'colsample_bylevel': 1,'colsample_bynode': 1,'min_child_weight': 1.25,'n_jobs': 11,'validate_parameters': true

7. Select Submit to update the solution definition.

## Resultado

The setting appears as a row on the Advanced Solution Setting tab on your solution's form.

## O que Fazer Depois

Retrain your solution.

You can also pass in this dictionary with a script. Be sure to use the enclosing label, braces, and parentheses when scripting.  
Script example:

    config.setModelParams({                   
                        'max_depth': 10,                   
                        'learning_rate': 0.1,
                        'objective': 'multi:softprob',
                        'num_class': 11,
                        'random_state': 10,
                        'eval_metric': 'mlogloss',
                        'booster': 'gbtree',
                        'n_estimators': 150,
                        'verbosity': 1,
                        'use_label_encoder': false,
                        'tree_method': 'hist',
                        'num_parallel_tree': 1,
                        'gamma': 0.25,
                        'subsample': 0.8,
                        'max_delta_step': 5,
                        'reg_alpha': 0,
                        'reg_lambda': 1,
                        'colsample_bytree': 0.8,
                        'colsample_bylevel': 1,
                        'colsample_bynode': 1,
                        'min_child_weight': 1.25,
                        'n_jobs': 11,
                        'validate_parameters': true
                        });

JSON example:

            
                {                    
                  "classification_model_params": {
                    'max_depth': 10,
                    'learning_rate': 0.1,
                    'objective': 'multi:softprob',
                    'num_class': 11,
                    'random_state': 10,
                    'eval_metric': 'mlogloss',
                    'booster': 'gbtree',
                    'n_estimators': 150,
                    'verbosity': 1,
                    'use_label_encoder': false,
                    'tree_method': 'hist',
                    'num_parallel_tree': 1,
                    'gamma': 0.25,
                    'subsample': 0.8,
                    'max_delta_step': 5,
                    'reg_alpha': 0,
                    'reg_lambda': 1,
                    'colsample_bytree': 0.8,
                    'colsample_bylevel': 1,
                    'colsample_bynode': 1,
                    'min_child_weight': 1.25,
                    'n_jobs': 11,
                    'validate_parameters': true
                    }
                  }

**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.")