ClassificationSolutionVersion - Global

concept australia Updated Sep 10, 2026 12 min read ClassificationSolutionVersionAPI.md
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The ClassificationSolutionVersion 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.

This API is used for working with solution versions based on ClassificationSolution API objects in the ClassificationSolution store.

The system creates a solution version each time you train a solution definition. Most versions are created during scheduled solution training.

Methods in this API are accessible using the following ClassificationSolution - Global methods:

Parent Topic:Server API reference

ClassificationSolutionVersion - getPredictionPerformance(Object options)

Calculates solution precision and coverage.

To use this method, you must first create settings using the setPredictionSettings() method.

Name

Type

Description

options

Object

Selects a metric to retrieve within a span of dates. For more information, see Configuring target metrics.``` { "fromDate": "String", "metricName": "String", "toDate": "String" }

Text
</td></tr><tr><td>
options.fromDate
</td><td>
String
</td><td>
Optional. Start date from which to retrieve metrics in system date time format.
</td></tr><tr><td>
options.metricName
</td><td>
String
</td><td>
Name of the metric.Valid values:
-   **precision**-   **coverage**
</td></tr><tr><td>
options.toDate
</td><td>
String
</td><td>
Optional. End date from which to retrieve metrics.Formnt: System date time format.
</td></tr></tbody></table><table id="table_hby_x2w_plb" class="returns"><thead><tr><th>
Type
</th><th>
Description
</th></tr></thead><tbody><tr><td>
Object
</td><td>
Parseable JavaScript object containing the results for the input metric.```{  "metricName" : "String",  "metricValue" : "String",  "numberOfPredictionsConsidered" : "String"}

Object.metricName

Name of metric selected as input.Data type: String

Object.metricValue

Value of the selected metric.Data type: String

Object.numberOfRecordsConsidered

Number of records in the Predictor Results [ml_predictor_results] table considered for calculations.Data type: String
The following example shows how to get a precision value from the active version of a solution.
JavaScript
// Get precision valuevar mlSolution = sn_ml.ClassificationSolutionStore.get('ml_incident_categorization');
gs.print(JSON.stringify(JSON.parse(mlSolution.getActiveVersion().getPredictionPerformance({"metricName": "precision"})), null, 2));

Output:

JSON
{  "metricName": "precision",  "metricValue": "70.10",  "numberOfPredictionsConsidered": "10"}

ClassificationSolutionVersion - getPredictionSettings()

Gets estimated precision, estimated coverage, estimated recall values from the ML Solutions [ml_solution] table and records from the Class Confidence [ml_class] table.

Name Type Description
None

Type

Description

Object

Returns prediction settings for each class. Data includes precision, coverage, recall, and distribution values of all classes included in the trained model from the ML Solutions [ml_solution] table. For details, see Predictive model components.``` { "": {Object} }

Text
</td></tr><tr><td>
&lt;Object&gt;.&lt;class name&gt;
</td><td>
Training statistics data from the Class Confidence \[ml\_class\] table. For more information, see [Configuring target metrics](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/intelligent-experiences/configuring-target-metrics-trained-classification-solution.md).Data type: Object

{ "precision": "String", "coverage": "String", "recall": "String" }

Text
</td></tr><tr><td>
&lt;Object&gt;.&lt;class name&gt;.precision
</td><td>
Estimated solution precision metric for this class.Data type: String \(numerical value\)
</td></tr><tr><td>
&lt;Object&gt;.&lt;class name&gt;.coverage
</td><td>
Estimated solution coverage metric for this class.Data type: String \(numerical value\)
</td></tr><tr><td>
&lt;Object&gt;.&lt;class name&gt;.recall
</td><td>
Estimated solution recall metric for this class.Data type: String \(numerical value\)
</td></tr></tbody></table>The following example shows how to get prediction settings of an active solution version.

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

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

Text
Output:

{ "solutionSettings": { "precision": "61.69", "coverage": "66.75", "recall": "57.54" }, "classSettings": { "Approvals": { "precision": "100", "coverage": "0", "recall": "0" }, "Authentication": { "precision": "38.71", "coverage": "98.44", "recall": "56.25" }, "Automation": { "precision": "100", "coverage": "0", "recall": "0" }, ...

Text
## ClassificationSolutionVersion - getProperties\(\)
Gets solution object properties and version number.
|Name|Type|Description||----|----|-----------||None| | |
<table id="table_e2m_zwz_4lb" class="returns"><thead><tr><th>
Type
</th><th>
Description
</th></tr></thead><tbody><tr><td>
Object
</td><td>
Contents of the Dataset and [ClassificationSolution](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/api-reference/server-api-reference/ClassificationSolutionAPI.md) version details. Results vary by object property setup.```{  "algorithmConfig": {    "algorithm": "String",    "targetClassRecall": "String"  },  "datasetProperties": {Object},  "domainName": "String",  "encoder": {Object},  "groupByFieldName": "String",     "inputFieldNames": [Array],  "isActive": Boolean,  "label": "String",  "name": "String",  "predictedFieldName": "String",  "processingLanguage": "String",  "scope": "String",  "stopwords": [Array],  "stopwords": [Array],  "trainingFrequency": "String",  "versionNumber": "Number"}

<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.

Data type: Object

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

<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. Data type: Array of Objects

JSON
[  {    "name": "String",    "type": "String"  }]

<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>.isActive

Flag that indicates whether this version is active.Valid values:

  • true: Version is active.
  • false: Version is not active.

Data type: String

<Object>.label

Identifies the prediction task. ``` { "label": "my first prediction" }

Text
Data type: String
</td></tr><tr><td>
&lt;Object&gt;.name
</td><td>
System-assigned name. Data type: String
</td></tr><tr><td>
&lt;Object&gt;.predictedFieldName
</td><td>
Identifies a field to be trained for predictability. Data type: String
</td></tr><tr><td>
&lt;Object&gt;.processingLanguage
</td><td>
Processing language in two-letter ISO 639-1 language code format. Data type: String
</td></tr><tr><td>
&lt;Object&gt;.scope
</td><td>
Object scope. Currently the only valid value is `global`.Data type: String
</td></tr><tr><td>
&lt;Object&gt;.stopwords
</td><td>
Optional. Preset list of strings that the system automatically generates based on the **language** property setting. For details, see [Create a custom stopwords list](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/intelligent-experiences/create-custom-stopwords-list.md). Data type: Array
</td></tr><tr><td>
&lt;Object&gt;.trainingFrequency
</td><td>
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
</td></tr><tr><td>
&lt;Object&gt;.versionNumber
</td><td>
Version number of the ClassificationSolution object.Data type: String
</td></tr></tbody></table>The following example gets properties of the active object version in the store.

// Get properties var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_incident_categorization');

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

Text
Output:

*** Script: { "datasetProperties": { "encodedQuery": "activeANYTHING^EQ", "fieldNames": [ "short_description", "category" ], "tableName": "incident" }, "domainName": "global", "inputFieldNames": [ "short_description" ], "isActive": "true", "label": "Incident Categorization_Trainer", "name": "ml_incident_categorization", "predictedFieldName": "category", "processingLanguage": "en", "stopwords": [ "Default English Stopwords" ], "versionNumber": "1" }

Text
## ClassificationSolutionVersion - getStatus\(Boolean includeDetails\)
Gets training completion status.
<table id="table_xfh_vbw_plb" class="parameters"><thead><tr><th>
Name
</th><th>
Type
</th><th>
Description
</th></tr></thead><tbody><tr><td>
includeDetails
</td><td>
Boolean
</td><td>
Flag that indicates whether to return status **details**.Valid values:
-   true: Return additional details.-   false: Don't return additional details.
Default: False
</td></tr></tbody></table><table id="table_yfh_vbw_plb" class="returns"><thead><tr><th>
Type
</th><th>
Description
</th></tr></thead><tbody><tr><td>
Object
</td><td>
JavaScript object containing training status information for a [ClassificationSolution](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/api-reference/server-api-reference/ClassificationSolutionAPI.md) object.```{  "state": "String",  "percentComplete": "Number as a String",  "hasJobEnded": "Boolean value as a String",  "details": {Object}}

<Object>.state

Training completion state. If the training job reaches a terminal state, the job does not leave that state. If the state is terminal, the hasJobEnded property is set to true.Possible values:

  • fetching_files_for_training
  • preparing_data
  • retry
  • solution_cancelled (terminal)
  • solution_complete(terminal)
  • solution_error (terminal)
  • solution_incomplete
  • training_request_received
  • training_request_timed_out (terminal)
  • training_solution
  • uploading_solution
  • waiting_for_training

Data type: String

<Object>.hasJobEnded

Flag that indicates whether training is complete.Valid values:

  • true: Training is complete.
  • false: Training is incomplete.

Data type: Boolean value as a String

<Object>.percentComplete

Training percent complete. If the completion percentage is less than 100, the job might be in a terminal state. For example, if training times out.Data type: Number as a String

Range: 0 thru 100

<Object>.details

Object containing a list of additional training details.Data type: Object
The following example shows a successful result with training complete.
JavaScript
// Get statusvar mlSolution = sn_ml.ClassificationSolutionStore.get('ml_incident_categorization');
gs.print(JSON.stringify(JSON.parse(mlSolution.getActiveVersion().getStatus(true), null, 2)));

Output:

JavaScript
{ "state":"solution_complete", "percentComplete":"100", "hasJobEnded":"true", "details":{"stepLabel":"Solution Complete"} // This information is only returned if getStatus(true);}

The following example shows an unsuccessful result with training complete.

JavaScript
// Get statusvar solutionName = 'ml_x_snc_global_global_classification_solution';var mlSolution = sn_ml.ClassificationSolutionStore.get(solutionName);var trainingStatus = mlSolution.getLatestVersion().getStatus();
gs.print(JSON.stringify(JSON.parse(trainingStatus), null, 2));

Output:

JSON
{ "state":"solution_error", "percentComplete":"100", "hasJobEnded":"true"}

ClassificationSolutionVersion - getTrainingStatistics()

Gets all the training statistics for a given solution.

Name Type Description
None

Type

Description

Object

JavaScript object with statistics for each class included in training and values for each class that was excluded from training. For details, see Predictive model components.``` { "includedClasses": {Object}, "excludedClasses": {Object} }

Text
</td></tr><tr><td>
&lt;Object&gt;.includedClasses
</td><td>
Object containing precision, coverage, recall, and distribution values of each class in the trained model.Data type: Object

"includedClasses": { "": { "distribution": "String", "statistics": [Array] },

Text
</td></tr><tr><td>
&lt;Object&gt;.includedClasses.&lt;class name&gt;.distribution
</td><td>
Value of the outcome probability distribution for this class.Data type: String
</td></tr><tr><td>
&lt;Object&gt;.includedClasses.&lt;class name&gt;.statistics
</td><td>
List of objects providing training statistics data. For more information, see [Configuring target metrics](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/intelligent-experiences/configuring-target-metrics-trained-classification-solution.md).Data type: Array of Objects

[ { "coverage": "String", "precision": "String", "recall": "String", "selected": "String" } ]

Text
</td></tr><tr><td>
&lt;Object&gt;.includedClasses.&lt;class name&gt;.statistics.coverage
</td><td>
Estimated solution coverage metric for this class.Data type: String \(numerical value\)
</td></tr><tr><td>
&lt;Object&gt;.includedClasses.&lt;class name&gt;.statistics.precision
</td><td>
Estimated solution precision metric for this class.Data type: String \(numerical value\)
</td></tr><tr><td>
&lt;Object&gt;.includedClasses.&lt;class name&gt;.statistics.recall
</td><td>
Estimated solution recall metric for this class.Data type: String \(numerical value\)
</td></tr><tr><td>
&lt;Object&gt;.includedClasses.&lt;class name&gt;.statistics.selected
</td><td>
Flag that indicates whether this class contains records included in solution training.Valid values:
-   true: This class contains records selected for training the solution.-   false: This class is excluded from training.
Data type: String \(Boolean value\)
Default: false
</td></tr><tr><td>
&lt;Object&gt;.excludedClasses
</td><td>
Distribution and row count of all the classes excluded from training.Data type: Object

"excludedClasses": { "": { "distribution": "0.11", "rowCount": "9" }

Text
</td></tr><tr><td>
&lt;Object&gt;.excludedClasses.&lt;class name&gt;.distribution
</td><td>
Value of the outcome probability distribution for this class.Data type: String
</td></tr><tr><td>
&lt;Object&gt;.excludedClasses.&lt;class name&gt;.rowCount
</td><td>
Number of rows excluded from training.Data type: String \(numerical value\)
</td></tr></tbody></table>The following example shows how to get training statistics from the active solution.

// Get training stats var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_incident_categorization');

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

Text
Output:

{ "includedClasses": { "Approvals": { "distribution": "0.43", "statistics": [ { "precision": "100", "coverage": "0", "recall": "0", "selected": "true" } ] }, "Authentication": { "distribution": "4.3", "statistics": [ { "precision": "39.33", "coverage": "95.31", "recall": "54.69", "selected": "false" }, { "precision": "100", "coverage": "0", "recall": "0", "selected": "false" }, ... } ... "excludedClasses": { "ACE": { "distribution": "0.11", "rowCount": "9" }, "AHA": { "distribution": "0.01", "rowCount": "1" }, ... }

Text
## ClassificationSolutionVersion - getVersionNumber\(\)
Gets the version number of a solution object.
|Name|Type|Description||----|----|-----------||None| | |
|Type|Description||----|-----------||String|Version number.|
The following example shows how to get a version number.

// Get version number var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_incident_categorization');

gs.print("Version number: "+JSON.stringify(JSON.parse(mlSolution.getActiveVersion().getVersionNumber()), null, 2));

Text
Output:

Version number: 1

Text
## ClassificationSolutionVersion - predict\(Object input, Object options\)
Gets the input data for a prediction.
<table id="table_wwz_pbw_plb" class="parameters"><thead><tr><th>
Name
</th><th>
Type
</th><th>
Description
</th></tr></thead><tbody><tr><td>
input
</td><td>
Object
</td><td>
[GlideRecord](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/australia/api-reference/server-api-reference/c_GlideRecordAPI.md) or array of JSON objects containing field names and values as key-value pairs.
</td></tr><tr><td>
options
</td><td>
Object
</td><td>
Optional values for filtering prediction results.```{  "apply_threshold": Boolean,  "top_n": Number}

options.apply_threshold

Boolean

Flag that indicates whether to check the threshold value for the solution and apply it to the result set.Valid values:

  • true: Return results in which confidence is greater than threshold.
  • false: Return all results.

Default: True

options.top_n

Number

If provided, returns the top results, up to the specified number of predictions.

Type

Description

Object

JSON object containing the prediction results sorted by sys_id or record_number. ``` { : [Array] }

Text
</td></tr><tr><td>
&lt;Object&gt;.&lt;identifier&gt;
</td><td>
List of objects with details for each prediction result.Data type: Array of Objects

: [ { "confidence": Number, "predictedSysId": "String", "predictedValue": "String", "threshold": Number } ]

Text
</td></tr><tr><td>
&lt;Object&gt;.&lt;identifier&gt;.&lt;object&gt;.confidence
</td><td>
Value of the confidence associated with the prediction. For example, 53.84. Data type: Number
</td></tr><tr><td>
&lt;Object&gt;.&lt;identifier&gt;.&lt;object&gt;.predictedSysId
</td><td>
The sys\_id of the predicted value. Results can be from any table on which information is being predicted. Data type: String
</td></tr><tr><td>
&lt;Object&gt;.&lt;identifier&gt;.&lt;object&gt;.predictedValue
</td><td>
Value representing the prediction result. Data type: String
</td></tr><tr><td>
&lt;Object&gt;.&lt;identifier&gt;.&lt;object&gt;.threshold
</td><td>
Value of the configured threshold associated with the prediction. Data type: Number
</td></tr></tbody></table>The following example shows how to display prediction results for a predict\(\) method that takes a GlideRecord by sys\_id for input and includes optional parameters to restrict to top three results and exclude the threshold value.

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

// single GlideRecord input var input = new GlideRecord("incident"); input.get("<sys_id>");

// configure optional parameters var options = {}; options.top_n = 3; options.apply_threshold = false;

var results = mlSolution.getVersion(1).predict(input, options); // pretty print JSON results gs.print(JSON.stringify(JSON.parse(results), null, 2));

Text
Output:

{ "<sys_id/gr>": [ { "confidence": 62.10782320780268, "threshold": 20.36, "predictedValue": "Clone Issues", "predictedSysId": "" }, { "confidence": 6.945237375770391, "threshold": 16.63, "predictedValue": "Instance Administration", "predictedSysId": "" }, { "confidence": 5.321061076300759, "threshold": 23.7, "predictedValue": "Administration", "predictedSysId": "" } ] }

CSS
The following example shows how to display prediction results for a predict\(\) method that takes an array of field names as key-value pairs for input and includes optional parameters to restrict to top three results and exclude the threshold value.

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

// key-value pairs input var input = [{"short_description":"my email is not working"}, {short_description:"need help with password"}];

// configure optional parameters var options = {}; options.top_n = 3; options.apply_threshold = false; var results = mlSolution.predict(input, options);

// pretty print JSON results gs.print(JSON.stringify(JSON.parse(results), null, 2));

Text
Output:

{ "1": [ { "confidence": 37.5023032262591, "threshold": 10.72, "predictedValue": "Authentication", "predictedSysId": "" }, { "confidence": 24.439964862166583, "threshold": 23.7, "predictedValue": "Administration", "predictedSysId": "" }, { "confidence": 11.736320486031047, "threshold": 100, "predictedValue": "Security", "predictedSysId": "" } ], "2": [ { "confidence": 99, "threshold": 17.77, "predictedValue": "Email", "predictedSysId": "" }, { "confidence": 3.182137005157543, "threshold": 10.72, "predictedValue": "Authentication", "predictedSysId": "" }, { "confidence": 2.8773826570713514, "threshold": -1, "predictedValue": "Email (I/f)", "predictedSysId": "" } ] }

Text
## ClassificationSolutionVersion - setPredictionSettings\(Object options\)
Sets precision, coverage, or recall values at solution level or class level.
<table id="table_b45_tdw_plb" class="parameters"><thead><tr><th>
Name
</th><th>
Type
</th><th>
Description
</th></tr></thead><tbody><tr><td>
options
</td><td>
Object
</td><td>
Metric configuration values. For information about these features, see [Configuring target metrics](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/australia/markdown/intelligent-experiences/configuring-target-metrics-trained-classification-solution.md).```{  	    "metricName" : "String",   "metricValue" : "String",   "className" : "String"}

options.metricName

String

Name of the metric to set.Valid values:

  • coverage
  • precision
  • recall

options.metricValue

String

Numeric value to assign to the metric.

options.className

String

Optional. Name of the class to restrict results to. Use the getTrainingStatistics() method to retrieve a complete list of classes for a solution.
|Type|Description| |----|-----------| |None| |

The following example shows how to set a precision metric to 89.5 for a class named Clone Issues.

JavaScript
var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_x_snc_global_global_classification');var input = {"metricName" : "precision", "metricValue" : "89.5", "className" : "Clone Issues"};
mlSolution.getActiveVersion().setTrainingStatistics(input);
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