--- 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 --- # ClassificationSolutionVersion - Global # ClassificationSolutionVersion - Global {#ariaid-title1} * Release version: Xanadu * * Updated August 1, 2024 * * ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 11 minutes to read 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](https://servicenow-prod.fluidtopics.net/n0ITHoNNnE5DnGxbNhqWtg#ClassificationSolutionAPI "The ClassificationSolution API is a scriptable object used in Predictive Intelligence stores.") objects in the [ClassificationSolution store](https://servicenow-prod.fluidtopics.net/qGtKN6FosUL3scH7MfLfHw#ClassificationSolutionStoreAPI "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 [ClassificationSolution - Global](https://servicenow-prod.fluidtopics.net/n0ITHoNNnE5DnGxbNhqWtg#ClassificationSolutionAPI "The ClassificationSolution API is a scriptable object used in Predictive Intelligence stores.") methods: * [getActiveVersion()](https://servicenow-prod.fluidtopics.net/n0ITHoNNnE5DnGxbNhqWtg#ClassifictnSol-getActiveVersion "Gets the active ClassificationSolutionVersion object.") * [getAllVersions()](https://servicenow-prod.fluidtopics.net/n0ITHoNNnE5DnGxbNhqWtg#ClassifictnSol-getAllVersions "Gets all versions of a classification solution.") * [getLatestVersion()](https://servicenow-prod.fluidtopics.net/n0ITHoNNnE5DnGxbNhqWtg#ClassifictnSol-getLatestVersion "Gets the latest version of a solution.") * [getVersion()](https://servicenow-prod.fluidtopics.net/n0ITHoNNnE5DnGxbNhqWtg#ClassifictnSol-getVersion_S "Gets a solution by provided version number.") {#ClassificationSolutionVersionAPI__ul_ycl_fzf_tlb} ## ClassificationSolutionVersion - getPredictionPerformance(Object options) {#ariaid-title2} Calculates solution precision and coverage. To use this method, you must first create settings using the setPredictionSettings() method. {#ClssfnV-getPredictionPerformance_O__table_gby_x2w_plb__entry__3} | Name | Type | Description | |-|-|-| | options | Object | Selects a metric to retrieve within a span of dates. For more information, see [Configuring target metrics](https://www.servicenow.com/docs/access?context=configuring-target-metrics-trained-classification-solution&version=xanadu&pubname=xanadu-intelligent-experiences&ft:locale=en-US). { "fromDate": "String", "metricName": "String", "toDate": "String" } | | options.fromDate | String | Optional. Start date from which to retrieve metrics in system date time format. | | options.metricName | String | Name of the metric. Valid values: * precision * coverage {#ClssfnV-getPredictionPerformance_O__ul_ncd_ldx_plb} | | options.toDate | String | Optional. End date from which to retrieve metrics. Formnt: System date time format. | [Table 1. Parameters] {#ClssfnV-getPredictionPerformance_O__table_gby_x2w_plb} {#ClssfnV-getPredictionPerformance_O__table_hby_x2w_plb__entry__2} | Type | Description | |-|-| | Object | 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 | [Table 2. Returns] {#ClssfnV-getPredictionPerformance_O__table_hby_x2w_plb} The following example shows how to get a precision value from the active version of a solution. // Get precision value var mlSolution = sn_ml.ClassificationSolutionStore.get('ml_incident_categorization'); gs.print(JSON.stringify(JSON.parse(mlSolution.getActiveVersion().getPredictionPerformance({"metricName": "precision"})), null, 2)); Output: { "metricName": "precision", "metricValue": "70.10", "numberOfPredictionsConsidered": "10" } ## ClassificationSolutionVersion - getPredictionSettings() {#ariaid-title3} Gets estimated precision, estimated coverage, estimated recall values from the ML Solutions \[ml_solution\] table and records from the Class Confidence \[ml_class\] table. {#ClssfnV-getPredictionSettings__table_eq3_xbw_plb__entry__3} | Name | Type | Description | |-|-|-| | None | | | [Table 3. Parameters] {#ClssfnV-getPredictionSettings__table_eq3_xbw_plb} {#ClssfnV-getPredictionSettings__table_wph_2dw_plb__entry__2} | 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](https://www.servicenow.com/docs/access?context=predictive-intelligence&version=xanadu&pubname=xanadu-intelligent-experiences&ft:locale=en-US). { "": {Object} } | | \.\ | Training statistics data from the Class Confidence \[ml_class\] table. For more information, see [Configuring target metrics](https://www.servicenow.com/docs/access?context=configuring-target-metrics-trained-classification-solution&version=xanadu&pubname=xanadu-intelligent-experiences&ft:locale=en-US). Data type: Object { "precision": "String", "coverage": "String", "recall": "String" } | | \.\.precision | Estimated solution precision metric for this class. Data type: String (numerical value) | | \.\.coverage | Estimated solution coverage metric for this class. Data type: String (numerical value) | | \.\.recall | Estimated solution recall metric for this class. Data type: String (numerical value) | [Table 4. Returns] {#ClssfnV-getPredictionSettings__table_wph_2dw_plb} 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)); 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" }, ... ## ClassificationSolutionVersion - getProperties() {#ariaid-title4} Gets solution object properties and version number. {#ClssfnV-getProperties__table_d2m_zwz_4lb__entry__3} | Name | Type | Description | |-|-|-| | None | | | [Table 5. Parameters] {#ClssfnV-getProperties__table_d2m_zwz_4lb} {#ClssfnV-getProperties__table_e2m_zwz_4lb__entry__2}{#ClssfnV-getProperties__classification-obj-group-desc} | Type | Description | |-|-| | Object | Contents of the Dataset and [ClassificationSolution](https://servicenow-prod.fluidtopics.net/n0ITHoNNnE5DnGxbNhqWtg#ClassifictnSol-ClassificationSolution_O "Creates a classification solution.") 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" } | | \.algorithmConfig | Method for encoding the solution. Data type: 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. {#ClssfnV-getProperties__ul_wtd_l2k_xlb} Data type: String | | \.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 | | \.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. Data type: Object { "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. Data type: Array of Objects [ { "name": "String", "type": "String" } ] | | \.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 | | \.groupByFieldName | Field name by which the system groups records to build classification solutions. Data type: String | | \.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. {#ClssfnV-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 | | \.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 | | \.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 {#ClssfnV-getProperties__ul_pmw_523_plb}Default: run_once Data type: String | | \.versionNumber | Version number of the ClassificationSolution object. Data type: String | [Table 6. Returns] {#ClssfnV-getProperties__table_e2m_zwz_4lb} 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)); 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" } ## ClassificationSolutionVersion - getStatus(Boolean includeDetails) {#ariaid-title5} Gets training completion status. {#ClssfnV-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. {#ClssfnV-getStatus_B__ul_n33_ckw_plb} Default: False | [Table 7. Parameters] {#ClssfnV-getStatus_B__table_xfh_vbw_plb} {#ClssfnV-getStatus_B__table_yfh_vbw_plb__entry__2} | Type | Description | |-|-| | Object | JavaScript object containing training status information for a [ClassificationSolution](https://servicenow-prod.fluidtopics.net/n0ITHoNNnE5DnGxbNhqWtg#ClassificationSolutionAPI "The ClassificationSolution 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 {#ClssfnV-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. {#ClssfnV-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 8. Returns] {#ClssfnV-getStatus_B__table_yfh_vbw_plb} The following example shows a successful result with training complete. // Get status var mlSolution = sn_ml.ClassificationSolutionStore.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_classification_solution'; var mlSolution = sn_ml.ClassificationSolutionStore.get(solutionName); var trainingStatus = mlSolution.getLatestVersion().getStatus(); gs.print(JSON.stringify(JSON.parse(trainingStatus), null, 2)); Output: { "state":"solution_error", "percentComplete":"100", "hasJobEnded":"true" } ## ClassificationSolutionVersion - getTrainingStatistics() {#ariaid-title6} Gets all the training statistics for a given solution. {#ClssfnV-getTrainingStatistics__table_vph_2dw_plb__entry__3} | Name | Type | Description | |-|-|-| | None | | | [Table 9. Parameters] {#ClssfnV-getTrainingStatistics__table_vph_2dw_plb} {#ClssfnV-getTrainingStatistics__table_wph_2dw_plb__entry__2} | 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](https://www.servicenow.com/docs/access?context=predictive-intelligence&version=xanadu&pubname=xanadu-intelligent-experiences&ft:locale=en-US). { "includedClasses": {Object}, "excludedClasses": {Object} } | | \.includedClasses | Object containing precision, coverage, recall, and distribution values of each class in the trained model. Data type: Object "includedClasses": { "": { "distribution": "String", "statistics": [Array] }, | | \.includedClasses.\.distribution | Value of the outcome probability distribution for this class. Data type: String | | \.includedClasses.\.statistics | List of objects providing training statistics data. For more information, see [Configuring target metrics](https://www.servicenow.com/docs/access?context=configuring-target-metrics-trained-classification-solution&version=xanadu&pubname=xanadu-intelligent-experiences&ft:locale=en-US). Data type: Array of Objects [ { "coverage": "String", "precision": "String", "recall": "String", "selected": "String" } ] | | \.includedClasses.\.statistics.coverage | Estimated solution coverage metric for this class. Data type: String (numerical value) | | \.includedClasses.\.statistics.precision | Estimated solution precision metric for this class. Data type: String (numerical value) | | \.includedClasses.\.statistics.recall | Estimated solution recall metric for this class. Data type: String (numerical value) | | \.includedClasses.\.statistics.selected | 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. {#ClssfnV-getTrainingStatistics__ul_sht_4cx_plb} Data type: String (Boolean value) Default: false | | \.excludedClasses | Distribution and row count of all the classes excluded from training. Data type: Object "excludedClasses": { "": { "distribution": "0.11", "rowCount": "9" } | | \.excludedClasses.\.distribution | Value of the outcome probability distribution for this class. Data type: String | | \.excludedClasses.\.rowCount | Number of rows excluded from training. Data type: String (numerical value) | [Table 10. Returns] {#ClssfnV-getTrainingStatistics__table_wph_2dw_plb} 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)); 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" }, ... } ## ClassificationSolutionVersion - getVersionNumber() {#ariaid-title7} Gets the version number of a solution object. {#ClssfnV-getVersionNumber__table_jsz_sbw_plb__entry__3} | Name | Type | Description | |-|-|-| | None | | | [Table 11. Parameters] {#ClssfnV-getVersionNumber__table_jsz_sbw_plb} {#ClssfnV-getVersionNumber__table_ksz_sbw_plb__entry__2} | Type | Description | |-|-| | String | Version number. | [Table 12. Returns] {#ClssfnV-getVersionNumber__table_ksz_sbw_plb} 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)); Output: Version number: 1 ## ClassificationSolutionVersion - predict(Object input, Object options) {#ariaid-title8} Gets the input data for a prediction. {#ClssfnV-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. {#ClssfnV-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 13. Parameters] {#ClssfnV-predict_O_O__table_wwz_pbw_plb} {#ClssfnV-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 14. Returns] {#ClssfnV-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.ClassificationSolutionStore.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)); Output: { "": [ { "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.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)); 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": "" } ] } ## ClassificationSolutionVersion - setPredictionSettings(Object options) {#ariaid-title9} Sets precision, coverage, or recall values at solution level or class level. {#ClssfnV-setPredictionSettings_O__table_b45_tdw_plb__entry__3} | Name | Type | Description | |-|-|-| | options | Object | Metric configuration values. For information about these features, see [Configuring target metrics](https://www.servicenow.com/docs/access?context=configuring-target-metrics-trained-classification-solution&version=xanadu&pubname=xanadu-intelligent-experiences&ft:locale=en-US). { "metricName" : "String", "metricValue" : "String", "className" : "String" } | | options.metricName | String | Name of the metric to set. Valid values: * coverage{#ClssfnV-setPredictionSettings_O__classification-v-metrics-coverage} * precision{#ClssfnV-setPredictionSettings_O__classification-v-metrics-precision} * recall{#ClssfnV-setPredictionSettings_O__classification-v-metrics-recall} {#ClssfnV-setPredictionSettings_O__ul_ncd_ldx_plb} | | 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()](https://servicenow-prod.fluidtopics.net/AL0AXV7FLmJf60UR28zYkg#ClssfnV-getTrainingStatistics "Gets all the training statistics for a given solution.") method to retrieve a complete list of classes for a solution. | [Table 15. Parameters] {#ClssfnV-setPredictionSettings_O__table_b45_tdw_plb} {#ClssfnV-setPredictionSettings_O__table_c45_tdw_plb__entry__2} | Type | Description | |-|-| | None | | [Table 16. Returns] {#ClssfnV-setPredictionSettings_O__table_c45_tdw_plb} The following example shows how to set a precision metric to 89.5 for a class named `Clone Issues`. 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);