--- sourceDocument: Xanadu API Reference sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/xanadu/api-reference Release : - xanadu ft:locale : - en-US ft:publication_title : - Xanadu API Reference ft:clusterId : - crapiref bundleId : - crapiref workflow : - Creator --- # RegressionSolutionVersion - Global # RegressionSolutionVersion - Global {#ariaid-title1} * Release version: Xanadu * * Updated August 1, 2024 * * ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 6 minutes to read The RegressionSolutionVersion 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. Use this API when working with solution versions based on [RegressionSolution API](https://servicenow-prod.fluidtopics.net/3hYy9ifcFaLlyL23bPPuBg#RegressionSolutionAPI "The RegressionSolution API is a scriptable object used in Predictive Intelligence stores.") objects in the [RegressionSolution store](https://servicenow-prod.fluidtopics.net/Uv9~kUPvNCgIVMkQc2v65A#RegressionSolutionStoreAPI "Enables storing and retrieving solutions."). 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 RegressionSolution methods: * [getActiveVersion()](https://servicenow-prod.fluidtopics.net/3hYy9ifcFaLlyL23bPPuBg#RegressionSol-getActiveVersion "Gets the active RegressionSolutionVersion object.") * [getAllVersions()](https://servicenow-prod.fluidtopics.net/3hYy9ifcFaLlyL23bPPuBg#RegressionSol-getAllVersions "Gets all versions of a RegressionSolution object.") * [getLatestVersion()](https://servicenow-prod.fluidtopics.net/3hYy9ifcFaLlyL23bPPuBg#RegressionSol-getLatestVersion "Gets the latest version of a solution.") * [getVersion()](https://servicenow-prod.fluidtopics.net/3hYy9ifcFaLlyL23bPPuBg#RegressionSol-getVersion_S "Gets a solution by provided version number.") {#RegressionSolutionVersionAPI__ul_ycl_fzf_tlb} ## RegressionSolutionVersion - getProperties() {#ariaid-title2} Gets solution object properties and version number. {#RegressionVersion-getProperties__table_d2m_zwz_4lb__entry__3} | Name | Type | Description | |-|-|-| | None | | | [Table 1. Parameters] {#RegressionVersion-getProperties__table_d2m_zwz_4lb} {#RegressionVersion-getProperties__table_e2m_zwz_4lb__entry__2} | Type | Description | |-|-| | Object | Contents of the Dataset and [RegressionSolution](https://servicenow-prod.fluidtopics.net/3hYy9ifcFaLlyL23bPPuBg#RegressionSol-RegressionSolution_O "Creates a regression solution.") version details. Results vary by object property setup. { "datasetProperties": {Object}, "domainName": "String", "encoder": {Object}, "inputFieldNames": [Array], "isActive": Boolean, "label": "String", "name": "String", "predictedFieldName": "String", "predictedInterval": [Array], "processingLanguage": "String", "scope": "String", "stopwords": [Array], "versionNumber": "Number" } | | \.datasetProperties | Lists the properties of the [DatasetDefinition()](https://servicenow-prod.fluidtopics.net/5PRN6njd2wF_we1ri_egPw#DDef-DatasetDefinition_O "Creates an instance of the DatasetDefinition class, enabling you to define a dataset by table name, fields, and query.") object associated with the solution. { "encodedQuery": "String", "fieldDetails": [Array], "fieldNames": [Array], "tableName": "String" } | | \.datasetProperties.tableName | Name of the table for the dataset. For example, `"tableName" : "Incident"`. Data type: String. | | \.datasetProperties.fieldNames | List of field names from the specified table as strings. For example, `"fieldNames" : ["short_description", "priority"]`. Data type: Array. | | \.datasetProperties.fieldNames.fieldDetails | List of JavaScript objects that specify field properties. [ { "name": "String", "type": "String" } ] Data type: Array. | | \.datasetProperties.fieldNames.fieldDetails.\.name | Name of the field defining the type of information to restrict this dataset to. Data type: String. | | \.datasetProperties.fieldDetails.\.type | Machine-learning field type. Data type: String. | | \.datasetProperties.fieldDetails.encodedQuery | Encoded query string in standard Glide format. See [Encoded query strings](https://www.servicenow.com/docs/access?context=c_EncodedQueryStrings&version=xanadu&pubname=xanadu-platform-user-interface&ft:locale=en-US). Data type: String. | | \.domainName | Domain name associated with this dataset. See [Domain separation and Predictive Intelligence](https://www.servicenow.com/docs/access?context=domain-separation-predictive-intelligence&version=xanadu&pubname=xanadu-intelligent-experiences&ft:locale=en-US). Data type: String. | | \.encoder | Encoder object assigned to this solution. See [Encoder - Encoder(Object config)](https://servicenow-prod.fluidtopics.net/lD2dU3P5hPYTlVdnWNZofg#Encoder-Encoder_O "Creates an encoder."). Data type: Object. | | \.inputFieldNames | List of input field names as strings. The model uses these fields used to make predictions. Data type: String. | | \.isActive | Flag that indicates whether this version is active. Valid values: * true: Version is active. * false: Version is not active. {#RegressionVersion-getProperties__ul_i5m_lp1_dmb} Data type: String | | \.label | Identifies the prediction task. { "label": "my first prediction" } Data type: String. | | \.name | System-assigned name. Data type: String. | | \.predictedFieldName | Identifies a field to be trained for predictability. Data type: String. | | \.predictedInterval | Range of values specifying the prediction confidence level. Data type: Array | | \.processingLanguage | Processing language in two-letter ISO 639-1 language code format. Data type: String. | | \.scope | Object scope. Currently the only valid value is `global`. Data type: String | | \.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](https://www.servicenow.com/docs/access?context=create-custom-stopwords-list&version=xanadu&pubname=xanadu-intelligent-experiences&ft:locale=en-US). Data type: Array. | | \.versionNumber | Version number of the RegressionSolution object. | [Table 2. Returns] {#RegressionVersion-getProperties__table_e2m_zwz_4lb} The following example gets properties of the active object version in the store. // Get properties var mlSolution = sn_ml.RegressionSolutionStore.get('ml_incident_categorization'); gs.print(JSON.stringify(JSON.parse(mlSolution.getActiveVersion().getProperties()), null, 2)); Output: { "datasetProperties": { "encodedQuery": "", "fieldNames": [ "short_description", "sourcedc", "targetdc", "dbsize", "duration" ], "tableName": "cloudinfratext" }, "domainName": "global", "encoderProperties": { "datasetsProperties": [], "name": "wc_regression" }, "inputFieldNames": [ "short_description", "sourcedc", "targetdc", "dbsize" ], "isActive": "true", "label": "Regression Test for DB Restore", "name": "ml_x_snc_global_global_regression", "predictedFieldName": "duration", "processingLanguage": "en", "stopwords": [ "Default English Stopwords" ], "versionNumber": "1" } ## RegressionSolutionVersion - getStatus(Boolean includeDetails) {#ariaid-title3} Gets training completion status. {#RegressionVersion-getStatus_B__table_xfh_vbw_plb__entry__3} | Name | Type | Description | |-|-|-| | includeDetails | Boolean | Flag that indicates whether to return status details. Valid values: * true: Return additional details. * false: Don't return additional details. {#RegressionVersion-getStatus_B__ul_n33_ckw_plb} Default: False | [Table 3. Parameters] {#RegressionVersion-getStatus_B__table_xfh_vbw_plb} {#RegressionVersion-getStatus_B__table_yfh_vbw_plb__entry__2} | Type | Description | |-|-| | Object | JavaScript object containing training status information for a [RegressionSolution](https://servicenow-prod.fluidtopics.net/3hYy9ifcFaLlyL23bPPuBg#RegressionSolutionAPI "The RegressionSolution API is a scriptable object used in Predictive Intelligence stores.") object. { "state": "String", "percentComplete": "Number as a String", "hasJobEnded": "Boolean value as a String", "details": {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 {#RegressionVersion-getStatus_B__ul_a5q_gh4_vlb} Data type: String | | \.hasJobEnded | Flag that indicates whether training is complete. Valid values: * true: Training is complete. * false: Training is incomplete. {#RegressionVersion-getStatus_B__ul_icy_zjx_rlb} Data type: Boolean value as a String | | \.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 | | \.details | Object containing a list of additional training details. Data type: Object | [Table 4. Returns] {#RegressionVersion-getStatus_B__table_yfh_vbw_plb} The following example shows a successful result with training complete. // Get status var mlSolution = sn_ml.RegressionSolutionStore.get('ml_incident_categorization'); gs.print(JSON.stringify(JSON.parse(mlSolution.getActiveVersion().getStatus(true), null, 2))); Output: { "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. // Get status var solutionName = 'ml_x_snc_global_global_regression_solution'; var mlSolution = sn_ml.RegressionSolutionStore.get(solutionName); var trainingStatus = mlSolution.getLatestVersion().getStatus(); gs.print(JSON.stringify(JSON.parse(trainingStatus), null, 2)); Output: { "state":"solution_error", "percentComplete":"100", "hasJobEnded":"true" } ## RegressionSolutionVersion - getVersionNumber() {#ariaid-title4} Gets the version number of a solution object. {#RegressionVersion-getVersionNumber__table_jsz_sbw_plb__entry__3} | Name | Type | Description | |-|-|-| | None | | | [Table 5. Parameters] {#RegressionVersion-getVersionNumber__table_jsz_sbw_plb} {#RegressionVersion-getVersionNumber__table_ksz_sbw_plb__entry__2} | Type | Description | |-|-| | String | Version number. | [Table 6. Returns] {#RegressionVersion-getVersionNumber__table_ksz_sbw_plb} The following example shows how to get a version number. // Get version number var mlSolution = sn_ml.RegressionSolutionStore.get('ml_incident_categorization'); gs.print("Version number: "+JSON.stringify(JSON.parse(mlSolution.getActiveVersion().getVersionNumber()), null, 2)); Output: Version number: 1 ## RegressionSolutionVersion - predict(Object input, Object options) {#ariaid-title5} Gets the input data for a prediction. {#RegressionVersion-predict_O_O__table_wwz_pbw_plb__entry__3} | Name | Type | Description | |-|-|-| | input | Object | [GlideRecord](https://servicenow-prod.fluidtopics.net/nRmUiaC7a08cr2N5iZGqoQ#c_GlideRecordAPI "The GlideRecord API is used for database operations.") or array of JSON objects containing field names and values as key-value pairs. | | options | Object | 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. {#RegressionVersion-predict_O_O__ul_f4d_xyx_slb} Default: True | | options.top_n | Number | If provided, returns the top results, up to the specified number of predictions. | [Table 7. Parameters] {#RegressionVersion-predict_O_O__table_wwz_pbw_plb} {#RegressionVersion-predict_O_O__table_xwz_pbw_plb__entry__2} | Type | Description | |-|-| | Object | JSON object containing the prediction results sorted by sys_id or record_number. { : [Array] } | | \.\ | List of objects with details for each prediction result. Data type: Array of Objects : [ { "confidence": Number, "predictedSysId": "String", "predictedValue": "String", "threshold": Number } ] | | \.\.\.confidence | Value of the confidence associated with the prediction. For example, 53.84. Data type: Number | | \.\.\.predictedSysId | The sys_id of the predicted value. Results can be from any table on which information is being predicted. Data type: String | | \.\.\.predictedValue | Value representing the prediction result. Data type: String | | \.\.\.threshold | Value of the configured threshold associated with the prediction. Data type: Number | [Table 8. Returns] {#RegressionVersion-predict_O_O__table_xwz_pbw_plb} 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.RegressionSolutionStore.get('ml_incident_categorization'); // single GlideRecord input var input = new GlideRecord("incident"); input.get(""); // 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)); { "": [ { "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": "" } ] } 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.RegressionSolutionStore.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)); { "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": "" } ] }