ClassificationSolution - Global

  • Release version: Australia
  • Updated March 12, 2026
  • 8 minutes to read

    • The ClassificationSolution API is a scriptable object used in Predictive Intelligence stores.

    This API requires the Predictive Intelligence plugin (com.glide.platform_ml) and is provided within the sn_ml namespace.

    The solution setup-to-training flow is as follows:
    1. Create a dataset using the DatasetDefinition API.
    2. Optional. Build an encoder using the Encoder API.
    3. Use the constructor to create a classification solution object.
    4. Add the solution object to the classification solution store using the ClassificationSolutionStore - add() method.
    5. Train the solution using the submitTrainingJob() method. This creates a version of the object that you can manage using the ClassificationSolutionVersion API.
    6. Get predictions using the ClassificationSolutionVersion – predict() method.
    Note:
    This API runs with full privileges before the Vancouver Patch 7 Hotfix 2b and Washington DC Patch 7 releases. With later releases, grant access using ACLs. For more information see Query ACLs.

    For usage guidelines, refer to Using ML APIs.

    ClassificationSolution - ClassificationSolution(Object config)

    Creates a classification solution.

    Table 1. Parameters
    Name Type Description
    config Object JavaScript object containing configuration properties of the solution.
    {
  "algorithmConfig": {Object},
  "dataset": {Object},
  "domainName": "String",
  "encoder": {Object},
  "groupByFieldName": "String", 
  "inputFieldNames": [Array],
  "label": "String",
  "minRowCount": "String",
  "predictedFieldName": "String",
  "processingLanguage": "String",
  "stopwords": [Array],
  "trainingFrequency": "String"
}
    config.algorithmConfig Object JavaScript object containing algorithm configuration properties. 
    'algorithmConfig' : {
  "algorithm": "String",
  "targetClassRecall": "String"
}
    config.algorithmConfig.algorithm String Method for encoding your solution.
    Possible values:
    • xgboost: XGBoost encoding to optimize the training.
    • logisticRegression: Method using the logistic regression model for categorical targets such as nominal or ordinal.
    config.algorithmConfig.targetClassRecall String Applies a class recall parameter to steer a solution's training to bias a specific class. Format is "<ClassName:RecallValue>" where the recall value is a number between 0 and 100 representing a percentage. For example, to set and apply this solution parameter to 90% accuracy for all records you train in the Email class, the value is set to "Email:90".
    config.dataset Object DatasetDefinition name.
    config.domainName String Optional. Domain name associated with this dataset.

    Default: Current domain, for example, "global".
    config.encoder Object Optional. Trained encoder object to assign to this solution. See Encoder - Encoder(Object config).
    config.explainability Boolean Flag that indicates whether to provide model explainability. Use model explainability to identify the importance of each input field to your model's predictions.
    Valid values:
    • true: The Classification model includes explainability details. Information can be viewed on the Feature Importance tab of the model's solution form.
    • false: The Classification model does not include explainability details.

    Default: false

    See also: Model Explainability
    config.groupByFieldName String Optional. Field name by which the system groups records to build classification solutions. For usage information, see Using group by for classification.
    config.inputFieldNames Array List of input field names as strings. The model uses these fields used to make predictions.
    config.label String Identifies the prediction task.
    config.minRowCount String Optional. Minimum number of records required in the dataset for training.

    Default: 10000
    config.predictedFieldName String Identifies a field to be trained for predictability.
    config.processingLanguage String Optional. Processing language in two-letter ISO 639-1 language code format.

    Default: "en"
    config.stopwords Array Optional. Preset list of strings that the system automatically generates based on the language property setting. For details, see Create a custom stopwords list.

    Default: English Stopwords
    config.trainingFrequency String The frequency to retrain the model.
    Possible values:
    • every_30_days
    • every_60_days
    • every_90_days
    • every_120_days
    • every_180_days
    • run_once
    Default: run_once

    The following example shows how to create an object and add it to the ClassificationSolution store.

    var myData = new sn_ml.DatasetDefinition(
  { 
     'tableName' : 'incident', 
     'fieldNames' : ['category', 'short_description', 'priority'],
     'fieldDetails' : [
       {
         'name' : 'category',
         'type' : 'nominal'
       },
       {
         'name' : 'short_description',
         'type' : 'text'
       }], 
     'encodedQuery' : 'activeANYTHING'
  });

var mySolution = new sn_ml.ClassificationSolution({
  'label': "my solution definition",
  'dataset' : myData,
  'predictedFieldName' : 'category',
  'inputFieldNames': ['short_description']
});

var myClassificationName = sn_ml.ClassificationSolutionStore.add(mySolution);

    ClassificationSolution - cancelTrainingJob()

    Cancels a job for a solution object that has been submitted for training.

    Table 2. Parameters
    Name Type Description
    None
    Table 3. Returns
    Type Description
    None

    The following example shows how to cancel an existing training job.

    var mySolution = sn_ml.ClassificationSolutionStore.get('ml_sn_global_global_classification');

mySolution.cancelTrainingJob();

    ClassificationSolution - getActiveVersion()

    Gets the active ClassificationSolutionVersion object.

    Table 4. Parameters
    Name Type Description
    None
    Table 5. Returns
    Type Description
    Object Active ClassificationSolutionVersion object.

    The following example shows how to get an active ClassificationSolution version from the store and return its training status.

    var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_x_snc_global_global_classification');

gs.print(JSON.stringify(JSON.parse(mlSolution.getActiveVersion().getStatus()), null, 2));

    Output:

    {
  "state": "solution_complete",
  "percentComplete": "100",
  "hasJobEnded": "true"
}

    ClassificationSolution - getAllVersions()

    Gets all versions of a classification solution.

    Table 6. Parameters
    Name Type Description
    None
    Table 7. Returns
    Type Description
    Array Existing versions of a solution object. See also ClassificationSolutionVersion API.

    The following example shows how to get all ClassificationSolution version objects and call the getVersionNumber() and getStatus() solution version methods on them.

    var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_x_snc_global_global_classification');

var mlSolutionVersions = mlSolution.getAllVersions();

for (i = 0; i < mlSolutionVersions.length; i++) {
gs.print("Version " + mlSolutionVersions[i].getVersionNumber() + " Status: " + mlSolutionVersions[i].getStatus() +"\n");
};

    Output:

    Version 3 Status: {"state":"solution_complete","percentComplete":"100","hasJobEnded":"true"}

Version 2 Status: {"state":"solution_complete","percentComplete":"100","hasJobEnded":"true"}

Version 1 Status: {"state":"solution_cancelled","percentComplete":"0","hasJobEnded":"true"}

    ClassificationSolution - getLatestVersion()

    Gets the latest version of a solution.

    Table 8. Parameters
    Name Type Description
    None
    Table 9. Returns
    Type Description
    Object ClassificationSolutionVersion object corresponding to the latest version of a ClassificationSolution().

    The following example shows how to get the latest version of a solution and return its training status.

    var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_x_snc_global_global_classification');

gs.print(JSON.stringify(JSON.parse(mlSolution.getLatestVersion().getStatus()), null, 2));

    Output:

    {
  "state": "solution_complete",
  "percentComplete": "100",
  "hasJobEnded": "true"
}

    ClassificationSolution - getName()

    Gets the name of the object to use for interaction with the store.

    Table 10. Parameters
    Name Type Description
    None
    Table 11. Returns
    Type Description
    String Name of the solution object.

    The following example shows how to update ClassificationSolution dataset information and print the name of the object.

    // Update solution
var myIncidentData = new sn_ml.DatasetDefinition({
   'tableName' : 'incident',
   'fieldNames' : ['category', 'short_description', 'priority'],
   'encodedQuery' : 'activeANYTHING'
});

var eligibleFields = JSON.parse(myIncidentData.getEligibleFields('classification'));

var myClassification = new sn_ml.ClassificationSolution({
   'label': "my classification solution",
   'dataset' : myIncidentData,
   'inputFieldNames': eligibleFields['eligibleInputFieldNames'],
   'predictedFieldName': 'category'
});

// update solution
sn_ml.ClassificationSolutionStore.update('ml_x_snc_global_global_my_solution_definition_4', myClassification);

// print solution name
gs.print('Solution Name: '+myClassification.getName());

    Output:

    Solution Name: ml_x_snc_global_global_my_solution_definition_4

    ClassificationSolution - getProperties()

    Gets solution object properties.

    Table 12. Parameters
    Name Type Description
    None
    Table 13. Returns
    Type Description
    Object Contents of the Dataset and ClassificationSolution() object details in the ClassificationSolutionStore.
    {
  "algorithmConfig": {
    "algorithm": "String",
    "targetClassRecall": "String"
  },
  "datasetProperties": {Object},
  "domainName": "String",
  "encoder": {Object},
  "groupByFieldName": "String", 
  "inputFieldNames": [Array],
  "label": "String",
  "name": "String",
  "predictedFieldName": "String",
  "processingLanguage": "String",
  "scope": "String",
  "stopwords": [Array],
  "trainingFrequency": "String"
}
    <Object>.algorithmConfig Method for encoding the solution.

    Data type: Object.
    <Object>.algorithmConfig.algorithm Name of the encoding algorithm for training this solution.
    Possible values:
    • xgboost: XGBoost encoding to optimize the training.
    • logisticRegression: Method using the logistic regression model for categorical targets such as nominal or ordinal.

    Data type: String.
    <Object>.algorithmConfig.targetClassRecall Class recall parameter to steer a solution's training to bias a specific class. The recall value is a number between 0 and 100 representing a percentage.

    Data type: String
    <Object>.datasetProperties

    Lists the properties of the DatasetDefinition() object associated with the solution.

    {
  "encodedQuery": "String",
  "fieldDetails": [Array],
  "fieldNames": [Array],
  "tableName": "String"
}

    Data type: Object.
    <Object>.datasetProperties.tableName Name of the table for the dataset. For example, "tableName" : "Incident".

    Data type: String.
    <Object>.datasetProperties.fieldNames List of field names from the specified table as strings. For example, "fieldNames" : ["short_description", "priority"].

    Data type: Array.
    <Object>.datasetProperties.fieldNames.fieldDetails List of JavaScript objects that specify field properties. 
    [
  {
    "name": "String",
    "type": "String"
  }
]

    Data type: Array.
    <Object>.datasetProperties.fieldNames.fieldDetails.<object>.name Name of the field defining the type of information to restrict this dataset to.

    Data type: String.
    <Object>.datasetProperties.fieldDetails.<object>.type Machine-learning field type.

    Data type: String.
    <Object>.datasetProperties.fieldDetails.encodedQuery Encoded query string in the standard platform format. See Encoded query strings.

    Data type: String.
    <Object>.domainName Domain name associated with this dataset. See Domain separation and Predictive Intelligence.

    Type: String
    <Object>.encoder Encoder object assigned to this solution. See Encoder - Encoder(Object config).

    Data type: Object.
    <Object>.groupByFieldName Field name by which the system groups records to build classification solutions.

    Data type: String
    <Object>.inputFieldNames List of input field names as strings. The model uses these fields used to make predictions.

    Data type: String.
    <Object>.label Identifies the prediction task. 
    {
  "label": "my first prediction"
}

    Data type: String.
    <Object>.name System-assigned name.

    Data type: String.
    <Object>.predictedFieldName Identifies a field to be trained for predictability.

    Data type: String.
    <Object>.processingLanguage Processing language in two-letter ISO 639-1 language code format.

    Data type: String.
    <Object>.scope Object scope. Currently the only valid value is global.

    Data type: String
    <Object>.stopwords Optional. Preset list of strings that the system automatically generates based on the language property setting. For details, see Create a custom stopwords list.

    Data type: Array.
    <Object>.trainingFrequency The frequency to retrain the model.
    Possible values:
    • every_30_days
    • every_60_days
    • every_90_days
    • every_120_days
    • every_180_days
    • run_once
    Default: run_once

    Data type: String.

    The following example gets properties of a solution object in the store.

    var mySolution = sn_ml.ClassificationSolutionStore.get('ml_sn_global_global_classification_solution');

gs.print(JSON.stringify(JSON.parse(mySolution.getProperties()), null, 2));
    Output:
    *** Script: {
  "datasetProperties": {
    "tableName": "incident",
    "fieldNames": [
      "category",
      "short_description",
      "priority",
      "assignment_group.name"
    ],
    "fieldDetails": [
      {
        "name": "category",
        "type": "nominal"
      },
      {
        "name": "short_description",
        "type": "text"
      }
    ]
  },
  "domainName": "global",
  "inputFieldNames": [
    "short_description"
  ],
  "label": "my solution definition",
  "name": "ml_x_snc_global_global_my_solution_definition_26",
  "predictedFieldName": "category",
  "processingLanguage": "en",
  "scope": "global",
  "stopwords": [
    "Default English Stopwords"
  ],
  "trainingFrequency": "run_once"
}

    ClassificationSolution - getVersion(String version)

    Gets a solution by provided version number.

    Table 14. Parameters
    Name Type Description
    version String Existing version number of a solution.
    Table 15. Returns
    Type Description
    Object Specified version of the ClassificationSolution() object on which you can call ClassificationSolutionVersion API methods.

    The following example shows how to get the training status of a solution by version number.

    var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_x_snc_global_global_classification');

gs.print(JSON.stringify(JSON.parse(mlSolution.getVersion('1').getStatus()), null, 2));

    Output:

    {
  "state": "solution_complete",
  "percentComplete": "100",
  "hasJobEnded": "true"
}

    ClassificationSolution - setActiveVersion(String version)

    Activates a specified version of a solution in the store.

    Table 16. Parameters
    Name Type Description
    version String Name of the ClassificationSolution() object version to activate.

    Activating this version deactivates any other version.
    Table 17. Returns
    Type Description
    None

    The following example shows how to activate a solution version in the store.

    sn_ml.ClassificationSolution.setActiveVersion("ml_incident_categorization");

    ClassificationSolution - submitTrainingJob()

    Submits a training job.

    Note:
    Before running this method, you must first add a solution to the store using the ClassificationSolutionStore - add() method.
    Table 18. Parameters
    Name Type Description
    None
    Table 19. Returns
    Type Description
    Object ClassificationSolutionVersion object corresponding to the ClassificationSolution being trained.

    The following example shows how to create a dataset, apply it to a solution, add the solution to a store, and submit the training job.

    // Create a dataset 
var myData = new sn_ml.DatasetDefinition({

  'tableName' : 'incident',
  'fieldNames' : ['assignment_group', 'short_description', 'description'],
  'encodedQuery' : 'activeANYTHING'

});

// Create a solution 
var mySolution = new sn_ml.ClassificationSolution({

  'label': "my solution definition",
  'dataset' : myData,
  'predictedFieldName' : 'assignment_group',
  'inputFieldNames':['short_description']

});

// Add the solution to the store to later be able to retrieve it.
var my_unique_name = sn_ml.ClassificationSolutionStore.add(mySolution);

// Train the solution - this is a long running job 
var myClassifierVersion = mySolution.submitTrainingJob();