PK YUaw'Ky y PDS4_ML_1J00_1100.JSON[
{
"dataDictionary": {
"Title": "PDS4 Data Dictionary" ,
"IM Version": "1.19.0.0" ,
"LDD Version": "1.1.0.0" ,
"Date": "2022-10-20T14:52:41" ,
"Description": "This document is a dump of the contents of the PDS4 Data Dictionary" ,
"namespaces": ["pds:", "ml:"] ,
"classDictionary": [
{
"class": {
"identifier": "0001_NASA_PDS_1.ml.Data_Set" ,
"title": "Data_Set" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.0.0.0" ,
"isAbstract": "true" ,
"isDeprecated": "false" ,
"description": "The Data_Set class is the container for classes and attributes describing the size and version of data sets used by the machine learning model."
, "associationList": [
{"association": {
"identifier": "0001_NASA_PDS_1.ml.Data_Set.ml.data_set_version_id" ,
"title": "data_set_version_id" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1010" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Data_Set.ml.data_set_version_id"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Data_Set.ml.data_set_count" ,
"title": "data_set_count" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1020" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Data_Set.ml.data_set_count"
]
}
}
]
}
}
, {
"class": {
"identifier": "0001_NASA_PDS_1.ml.Machine_Learning" ,
"title": "Machine_Learning" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.0.0.0" ,
"isAbstract": "false" ,
"isDeprecated": "false" ,
"description": "The Machine_Learning class is a container for all machine learning information in the label. "
, "associationList": [
{"association": {
"identifier": "0001_NASA_PDS_1.ml.Machine_Learning.ml.Trained_Machine_Learning_Model" ,
"title": "Trained_Machine_Learning_Model" ,
"assocType": "component_of" ,
"isAttribute": "false" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "*" ,
"classOrder": "1010" ,
"classId": [
"0001_NASA_PDS_1.ml.Trained_Machine_Learning_Model"
]
}
}
]
}
}
, {
"class": {
"identifier": "0001_NASA_PDS_1.ml.Machine_Learning_Algorithm" ,
"title": "Machine_Learning_Algorithm" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.0.0.0" ,
"isAbstract": "false" ,
"isDeprecated": "false" ,
"description": "The Machine_Learning_Algorithm class is a container for classes and and attributes describing the algorithm type and learning style used. An external reference to a citation for the algorithm is required."
, "associationList": [
{"association": {
"identifier": "0001_NASA_PDS_1.ml.Machine_Learning_Algorithm.ml.algorithm_learning_style" ,
"title": "algorithm_learning_style" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1010" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Machine_Learning_Algorithm.ml.algorithm_learning_style"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Machine_Learning_Algorithm.ml.algorithm_type" ,
"title": "algorithm_type" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1020" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Machine_Learning_Algorithm.ml.algorithm_type"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Machine_Learning_Algorithm.ml.algorithm_name" ,
"title": "algorithm_name" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1030" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Machine_Learning_Algorithm.ml.algorithm_name"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Machine_Learning_Algorithm.pds.External_Reference" ,
"title": "External_Reference" ,
"assocType": "component_of" ,
"isAttribute": "false" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "*" ,
"classOrder": "1040" ,
"classId": [
"0001_NASA_PDS_1.pds.External_Reference"
]
}
}
]
}
}
, {
"class": {
"identifier": "0001_NASA_PDS_1.ml.Test_Performance" ,
"title": "Test_Performance" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.0.0.0" ,
"isAbstract": "false" ,
"isDeprecated": "false" ,
"description": "The Test_Performance class contains information about a trained model's performance on the test set."
, "associationList": [
{"association": {
"identifier": "0001_NASA_PDS_1.ml.Test_Performance.ml.performance_measure" ,
"title": "performance_measure" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1010" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Test_Performance.ml.performance_measure"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Test_Performance.ml.performance_score" ,
"title": "performance_score" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1020" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Test_Performance.ml.performance_score"
]
}
}
]
}
}
, {
"class": {
"identifier": "0001_NASA_PDS_1.ml.Test_Set" ,
"title": "Test_Set" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.0.0.0" ,
"isAbstract": "false" ,
"isDeprecated": "false" ,
"description": "The Test_Set class belongs to the Data_Set class family and contains attributes describing the size and version of the data set used to test the machine learning model (i.e., in terms of generalization to previously unseen data)."
, "associationList": [
{"association": {
"identifier": "0001_NASA_PDS_1.ml.Data_Set.ml.data_set_version_id" ,
"title": "data_set_version_id" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1010" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Data_Set.ml.data_set_version_id"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Data_Set.ml.data_set_count" ,
"title": "data_set_count" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1020" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Data_Set.ml.data_set_count"
]
}
}
]
}
}
, {
"class": {
"identifier": "0001_NASA_PDS_1.ml.Trained_Machine_Learning_Model" ,
"title": "Trained_Machine_Learning_Model" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.0.0.0" ,
"isAbstract": "false" ,
"isDeprecated": "false" ,
"description": "The Trained_Machine_Learning_Model class is a container for information about how a given model was trained and evaluated. A Machine_Learning_Algorithm and Training_Set are required, while Validation_Set and Test_Set (and Test_Performance) are optional."
, "associationList": [
{"association": {
"identifier": "0001_NASA_PDS_1.ml.Trained_Machine_Learning_Model.ml.trained_model_version_id" ,
"title": "trained_model_version_id" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1010" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Trained_Machine_Learning_Model.ml.trained_model_version_id"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Trained_Machine_Learning_Model.ml.trained_model_name" ,
"title": "trained_model_name" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1020" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Trained_Machine_Learning_Model.ml.trained_model_name"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Trained_Machine_Learning_Model.ml.Machine_Learning_Algorithm" ,
"title": "Machine_Learning_Algorithm" ,
"assocType": "component_of" ,
"isAttribute": "false" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1030" ,
"classId": [
"0001_NASA_PDS_1.ml.Machine_Learning_Algorithm"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Trained_Machine_Learning_Model.ml.Training_Set" ,
"title": "Training_Set" ,
"assocType": "component_of" ,
"isAttribute": "false" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1040" ,
"classId": [
"0001_NASA_PDS_1.ml.Training_Set"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Trained_Machine_Learning_Model.ml.Validation_Set" ,
"title": "Validation_Set" ,
"assocType": "component_of" ,
"isAttribute": "false" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "0" ,
"maximumCardinality": "1" ,
"classOrder": "1050" ,
"classId": [
"0001_NASA_PDS_1.ml.Validation_Set"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Trained_Machine_Learning_Model.ml.Test_Set" ,
"title": "Test_Set" ,
"assocType": "component_of" ,
"isAttribute": "false" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "0" ,
"maximumCardinality": "1" ,
"classOrder": "1060" ,
"classId": [
"0001_NASA_PDS_1.ml.Test_Set"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Trained_Machine_Learning_Model.ml.Test_Performance" ,
"title": "Test_Performance" ,
"assocType": "component_of" ,
"isAttribute": "false" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "0" ,
"maximumCardinality": "*" ,
"classOrder": "1070" ,
"classId": [
"0001_NASA_PDS_1.ml.Test_Performance"
]
}
}
]
}
}
, {
"class": {
"identifier": "0001_NASA_PDS_1.ml.Training_Set" ,
"title": "Training_Set" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.0.0.0" ,
"isAbstract": "false" ,
"isDeprecated": "false" ,
"description": "The Training_Set class belongs to the Data_Set class family and contains attributes that describe the size and version of the data set used to train the machine learning model."
, "associationList": [
{"association": {
"identifier": "0001_NASA_PDS_1.ml.Data_Set.ml.data_set_version_id" ,
"title": "data_set_version_id" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1010" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Data_Set.ml.data_set_version_id"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Data_Set.ml.data_set_count" ,
"title": "data_set_count" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1020" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Data_Set.ml.data_set_count"
]
}
}
]
}
}
, {
"class": {
"identifier": "0001_NASA_PDS_1.ml.Validation_Set" ,
"title": "Validation_Set" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.0.0.0" ,
"isAbstract": "false" ,
"isDeprecated": "false" ,
"description": "The Validation_Set class belongs to the Data_Set class family and contains attributes that describe the size and version of the data set used to validate the machine learning model (e.g., to choose the best hyperparameters)."
, "associationList": [
{"association": {
"identifier": "0001_NASA_PDS_1.ml.Data_Set.ml.data_set_version_id" ,
"title": "data_set_version_id" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1010" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Data_Set.ml.data_set_version_id"
]
}
}
, {"association": {
"identifier": "0001_NASA_PDS_1.ml.Data_Set.ml.data_set_count" ,
"title": "data_set_count" ,
"assocType": "attribute_of" ,
"isAttribute": "true" ,
"isChoice": "false" ,
"isAny": "false" ,
"groupName": "null" ,
"minimumCardinality": "1" ,
"maximumCardinality": "1" ,
"classOrder": "1020" ,
"attributeId": [
"0001_NASA_PDS_1.ml.Data_Set.ml.data_set_count"
]
}
}
]
}
}
]
, "attributeDictionary": [
{
"attribute": {
"identifier": "0001_NASA_PDS_1.ml.Data_Set.ml.data_set_count" ,
"title": "data_set_count" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.19" ,
"description": "The data_set_count attribute provides the number of items in the data set." ,
"isNillable": "false" ,
"isEnumerated": "false" ,
"isDeprecated": "false" ,
"dataType": "ASCII_NonNegative_Integer" ,
"dataTypeId": "0001_NASA_PDS_1.pds.ASCII_NonNegative_Integer" ,
"minimumCharacters": "Unbounded" ,
"maximumCharacters": "Unbounded" ,
"minimumValue": "1" ,
"maximumValue": "18446744073709551615" ,
"pattern": "[0-9]+" ,
"unitOfMeasure": "null" ,
"unitOfMeasureId": "null" ,
"unitId": "null" ,
"defaultUnitId": "null"
}
}
, {
"attribute": {
"identifier": "0001_NASA_PDS_1.ml.Data_Set.ml.data_set_version_id" ,
"title": "data_set_version_id" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.19" ,
"description": "The data_set_version_id attribute specifies the data set version number." ,
"isNillable": "false" ,
"isEnumerated": "false" ,
"isDeprecated": "false" ,
"dataType": "ASCII_VID" ,
"dataTypeId": "0001_NASA_PDS_1.pds.ASCII_VID" ,
"minimumCharacters": "3" ,
"maximumCharacters": "100" ,
"minimumValue": "Unbounded" ,
"maximumValue": "Unbounded" ,
"pattern": "null" ,
"unitOfMeasure": "null" ,
"unitOfMeasureId": "null" ,
"unitId": "null" ,
"defaultUnitId": "null"
}
}
, {
"attribute": {
"identifier": "0001_NASA_PDS_1.ml.Machine_Learning_Algorithm.ml.algorithm_learning_style" ,
"title": "algorithm_learning_style" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.19" ,
"description": "The algorithm_learning_style attribute describes the type of learning style employed by the algorithm to solve a problem. Specifically, the learning style depends on whether labeled or unlabeled data was employed to train the model. Labeled data includes observations that are associated with a desired output such as a class or numeric value." ,
"isNillable": "false" ,
"isEnumerated": "true" ,
"isDeprecated": "false" ,
"dataType": "ASCII_Short_String_Collapsed" ,
"dataTypeId": "0001_NASA_PDS_1.pds.ASCII_Short_String_Collapsed" ,
"minimumCharacters": "1" ,
"maximumCharacters": "255" ,
"minimumValue": "Unbounded" ,
"maximumValue": "Unbounded" ,
"pattern": "null" ,
"unitOfMeasure": "null" ,
"unitOfMeasureId": "null" ,
"unitId": "null" ,
"defaultUnitId": "null"
, "PermissibleValueList": [
{"PermissibleValue": {
"value": "Semisupervised_Learning" ,
"valueMeaning": "Both labeled data and unlabeled data were used to inform the model." ,
"isDeprecated": "false"
}
}
, {"PermissibleValue": {
"value": "Supervised_Learning" ,
"valueMeaning": "Supervised_Learning indicates that labeled data has been used to train a model to yield the desired output." ,
"isDeprecated": "false"
}
}
, {"PermissibleValue": {
"value": "Unsupervised_Learning" ,
"valueMeaning": "The algorithm did not employ any labeled data and instead discovered patterns from unlabeled data." ,
"isDeprecated": "false"
}
}
]
}
}
, {
"attribute": {
"identifier": "0001_NASA_PDS_1.ml.Machine_Learning_Algorithm.ml.algorithm_name" ,
"title": "algorithm_name" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.19" ,
"description": "The algorithm_name attribute specifies the name of the algorithm used." ,
"isNillable": "false" ,
"isEnumerated": "false" ,
"isDeprecated": "false" ,
"dataType": "ASCII_Short_String_Collapsed" ,
"dataTypeId": "0001_NASA_PDS_1.pds.ASCII_Short_String_Collapsed" ,
"minimumCharacters": "1" ,
"maximumCharacters": "255" ,
"minimumValue": "Unbounded" ,
"maximumValue": "Unbounded" ,
"pattern": "null" ,
"unitOfMeasure": "null" ,
"unitOfMeasureId": "null" ,
"unitId": "null" ,
"defaultUnitId": "null"
}
}
, {
"attribute": {
"identifier": "0001_NASA_PDS_1.ml.Machine_Learning_Algorithm.ml.algorithm_type" ,
"title": "algorithm_type" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.19" ,
"description": "The algorithm_type attribute describes the kind of algorithm used, such as a regression model, neural network, tree, etc." ,
"isNillable": "false" ,
"isEnumerated": "true" ,
"isDeprecated": "false" ,
"dataType": "ASCII_Short_String_Collapsed" ,
"dataTypeId": "0001_NASA_PDS_1.pds.ASCII_Short_String_Collapsed" ,
"minimumCharacters": "1" ,
"maximumCharacters": "255" ,
"minimumValue": "Unbounded" ,
"maximumValue": "Unbounded" ,
"pattern": "null" ,
"unitOfMeasure": "null" ,
"unitOfMeasureId": "null" ,
"unitId": "null" ,
"defaultUnitId": "null"
, "PermissibleValueList": [
{"PermissibleValue": {
"value": "Convolutional_Neural_Network_Classifier" ,
"valueMeaning": "The algorithm used for data analysis is a Convolutional Neural Network (CNN) Classifier." ,
"isDeprecated": "false"
}
}
]
}
}
, {
"attribute": {
"identifier": "0001_NASA_PDS_1.ml.Test_Performance.ml.performance_measure" ,
"title": "performance_measure" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.19" ,
"description": "The performance_measure attribute specifies the name of the measure (or metric) used to report performance of the model on the test set." ,
"isNillable": "false" ,
"isEnumerated": "false" ,
"isDeprecated": "false" ,
"dataType": "ASCII_Short_String_Collapsed" ,
"dataTypeId": "0001_NASA_PDS_1.pds.ASCII_Short_String_Collapsed" ,
"minimumCharacters": "1" ,
"maximumCharacters": "255" ,
"minimumValue": "Unbounded" ,
"maximumValue": "Unbounded" ,
"pattern": "null" ,
"unitOfMeasure": "null" ,
"unitOfMeasureId": "null" ,
"unitId": "null" ,
"defaultUnitId": "null"
}
}
, {
"attribute": {
"identifier": "0001_NASA_PDS_1.ml.Test_Performance.ml.performance_score" ,
"title": "performance_score" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.19" ,
"description": "The performance_score attribute reports the numeric score the model achieved using performance_measure on the test set. Values are not constrained since the measure may not be a strict metric. Examples could include accuracy, loss, runtime, memory consumption, etc." ,
"isNillable": "false" ,
"isEnumerated": "false" ,
"isDeprecated": "false" ,
"dataType": "ASCII_Real" ,
"dataTypeId": "0001_NASA_PDS_1.pds.ASCII_Real" ,
"minimumCharacters": "Unbounded" ,
"maximumCharacters": "Unbounded" ,
"minimumValue": "-1.7976931348623157e308" ,
"maximumValue": "1.7976931348623157e308" ,
"pattern": "null" ,
"unitOfMeasure": "null" ,
"unitOfMeasureId": "null" ,
"unitId": "null" ,
"defaultUnitId": "null"
}
}
, {
"attribute": {
"identifier": "0001_NASA_PDS_1.ml.Trained_Machine_Learning_Model.ml.trained_model_name" ,
"title": "trained_model_name" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.19" ,
"description": "The trained_model_name attribute specifies the name of the model used." ,
"isNillable": "false" ,
"isEnumerated": "false" ,
"isDeprecated": "false" ,
"dataType": "ASCII_Short_String_Collapsed" ,
"dataTypeId": "0001_NASA_PDS_1.pds.ASCII_Short_String_Collapsed" ,
"minimumCharacters": "1" ,
"maximumCharacters": "255" ,
"minimumValue": "Unbounded" ,
"maximumValue": "Unbounded" ,
"pattern": "null" ,
"unitOfMeasure": "null" ,
"unitOfMeasureId": "null" ,
"unitId": "null" ,
"defaultUnitId": "null"
}
}
, {
"attribute": {
"identifier": "0001_NASA_PDS_1.ml.Trained_Machine_Learning_Model.ml.trained_model_version_id" ,
"title": "trained_model_version_id" ,
"registrationAuthorityId": "0001_NASA_PDS_1" ,
"nameSpaceId": "ml" ,
"steward": "img" ,
"versionId": "1.19" ,
"description": "The trained_model_version_id attribute specifies the trained model version number." ,
"isNillable": "false" ,
"isEnumerated": "false" ,
"isDeprecated": "false" ,
"dataType": "ASCII_VID" ,
"dataTypeId": "0001_NASA_PDS_1.pds.ASCII_VID" ,
"minimumCharacters": "3" ,
"maximumCharacters": "100" ,
"minimumValue": "Unbounded" ,
"maximumValue": "Unbounded" ,
"pattern": "null" ,
"unitOfMeasure": "null" ,
"unitOfMeasureId": "null" ,
"unitId": "null" ,
"defaultUnitId": "null"
}
}
]
}
}
]
PK YU]p2 p2 PDS4_ML_1J00_1100.xsd
This namespace enables the specification of metadata that
describes products generated by the use of a machine learning
model. It captures information about how the model was trained
and evaluated.
## CHANGE LOG ##
1.1.0.0
- Change data_set_size to data_set_count.
- Expand attribute definitions.
- Update steward name.
1.0.1.0
- Address Oxygen-flagged errors.
- Make data_set_size a ASCII_NonNegative_Integer.
- Update some attribute and class definitions.
- Update README file per latest template.
- Use PDS Information Model 1.18 (I).
1.0.0.0
- Initial release.
The Data_Set class is the container for classes
and attributes describing the size and version of data sets used
by the machine learning model.
The Machine_Learning class is a container for
all machine learning information in the label.
The Machine_Learning_Algorithm class is a
container for classes and and attributes describing the
algorithm type and learning style used. An external reference to
a citation for the algorithm is required.
The Test_Performance class contains information
about a trained model's performance on the test
set.
The Test_Set class belongs to the Data_Set class
family and contains attributes describing the size and version
of the data set used to test the machine learning model (i.e.,
in terms of generalization to previously unseen
data).
The Trained_Machine_Learning_Model class is a
container for information about how a given model was trained
and evaluated. A Machine_Learning_Algorithm and Training_Set are
required, while Validation_Set and Test_Set (and
Test_Performance) are optional.
The Training_Set class belongs to the Data_Set
class family and contains attributes that describe the size and
version of the data set used to train the machine learning
model.
The Validation_Set class belongs to the Data_Set
class family and contains attributes that describe the size and
version of the data set used to validate the machine learning
model (e.g., to choose the best
hyperparameters).
This section contains the simpleTypes that provide more constraints
than those at the base data type level. The simpleTypes defined here build on the base data
types. This is another component of the common dictionary and therefore falls within the
common namespace.
The algorithm_learning_style attribute describes
the type of learning style employed by the algorithm to solve a
problem. Specifically, the learning style depends on whether
labeled or unlabeled data was employed to train the model.
Labeled data includes observations that are associated with a
desired output such as a class or numeric
value.
The algorithm_name attribute specifies the name
of the algorithm used.
The algorithm_type attribute describes the kind
of algorithm used, such as a regression model, neural network,
tree, etc.
The data_set_count attribute provides the number
of items in the data set.
The data_set_version_id attribute specifies the
data set version number.
The performance_measure attribute specifies the
name of the measure (or metric) used to report performance of
the model on the test set.
The performance_score attribute reports the
numeric score the model achieved using performance_measure on
the test set. Values are not constrained since the measure may
not be a strict metric. Examples could include accuracy, loss,
runtime, memory consumption, etc.
The trained_model_name attribute specifies the
name of the model used.
The trained_model_version_id attribute specifies
the trained model version number.
PK YU\) \) PDS4_ML_1J00_1100.txtPDS4 Local Data Dictionary Processing Report
Configuration:
LDDTool Version 14.1.2
LDD Version Id: 1.1.0.0
LDD Label Version Id: 1.25
LDD Discipline (T/F): true
LDD Namespace URL: http://pds.nasa.gov/pds4/
LDD URN Prefix: urn:nasa:pds:
Time Thu Oct 20 21:52:41 UTC 2022
Common Schema [PDS4_PDS_1J00.xsd]
Common Schematron [PDS4_PDS_1J00.sch]
IM Version Id: 1.19.0.0
IM Namespace Id: pds
IM Label Version Id: 1.25
IM Object Model [UpperModel.pont]
IM Data Dictionary [dd11179.pins]
IM Configuration File [MDPTNConfigClassDisp.xml]
IM Glossary [Glossary.pins]
IM Document Spec [DMDocument.pins]
Parameters:
Input File [/home/runner/work/ldd-ml/ldd-ml/src/PDS4_ML_IngestLDD.xml]
PDS Processing true
LDD Processing true
Discipline LDD true
Mission LDD false
Write Attr Elements false
Merge with Master false
Summary:
Classes 8
Attributes 9
Associations 19
Error messages 0
Warning messages 0
Information messages 0
Detailed validation messages
Parsed Input - Header:
LDD Name Machine Learning Analysis
LDD Version 1.1.0.0
Full Name Sara A. Bond
Steward img
Namespace Id ml
Comment This namespace enables the specification of metadata that
describes products generated by the use of a machine learning
model. It captures information about how the model was trained
and evaluated.
## CHANGE LOG ##
1.1.0.0
- Change data_set_size to data_set_count.
- Expand attribute definitions.
- Update steward name.
1.0.1.0
- Address Oxygen-flagged errors.
- Make data_set_size a ASCII_NonNegative_Integer.
- Update some attribute and class definitions.
- Update README file per latest template.
- Use PDS Information Model 1.18 (I).
1.0.0.0
- Initial release.
Last Modification Time 2022-06-30
PDS4 Merge Flag false
Parsed Input - Attributes:
name data_set_count
version 1.19
value data type ASCII_NonNegative_Integer
description The data_set_count attribute provides the number of items in the data set.
minimum value 1
name data_set_version_id
version 1.19
value data type ASCII_VID
description The data_set_version_id attribute specifies the data set version number.
name algorithm_learning_style
version 1.19
value data type ASCII_Short_String_Collapsed
description The algorithm_learning_style attribute describes the type of learning style employed by the algorithm to solve a problem. Specifically, the learning style depends on whether labeled or unlabeled data was employed to train the model. Labeled data includes observations that are associated with a desired output such as a class or numeric value.
name algorithm_type
version 1.19
value data type ASCII_Short_String_Collapsed
description The algorithm_type attribute describes the kind of algorithm used, such as a regression model, neural network, tree, etc.
name algorithm_name
version 1.19
value data type ASCII_Short_String_Collapsed
description The algorithm_name attribute specifies the name of the algorithm used.
name performance_measure
version 1.19
value data type ASCII_Short_String_Collapsed
description The performance_measure attribute specifies the name of the measure (or metric) used to report performance of the model on the test set.
name performance_score
version 1.19
value data type ASCII_Real
description The performance_score attribute reports the numeric score the model achieved using performance_measure on the test set. Values are not constrained since the measure may not be a strict metric. Examples could include accuracy, loss, runtime, memory consumption, etc.
name trained_model_version_id
version 1.19
value data type ASCII_VID
description The trained_model_version_id attribute specifies the trained model version number.
name trained_model_name
version 1.19
value data type ASCII_Short_String_Collapsed
description The trained_model_name attribute specifies the name of the model used.
Parsed Input - Classes:
name Data_Set
description The Data_Set class is the container for classes and attributes describing the size and version of data sets used by the machine learning model.
is abstract true
is choice false
subclass of USER
Associations
local identifier data_set_version_id
minimum occurrences 1
maximum occurrences 1
reference type attribute_of
local identifier data_set_count
minimum occurrences 1
maximum occurrences 1
reference type attribute_of
name Training_Set
description The Training_Set class belongs to the Data_Set class family and contains attributes that describe the size and version of the data set used to train the machine learning model.
is abstract false
is choice false
subclass of Data_Set
Associations
local identifier Data_Set
minimum occurrences 1
maximum occurrences 1
reference type parent_of
name Validation_Set
description The Validation_Set class belongs to the Data_Set class family and contains attributes that describe the size and version of the data set used to validate the machine learning model (e.g., to choose the best hyperparameters).
is abstract false
is choice false
subclass of Data_Set
Associations
local identifier Data_Set
minimum occurrences 1
maximum occurrences 1
reference type parent_of
name Test_Set
description The Test_Set class belongs to the Data_Set class family and contains attributes describing the size and version of the data set used to test the machine learning model (i.e., in terms of generalization to previously unseen data).
is abstract false
is choice false
subclass of Data_Set
Associations
local identifier Data_Set
minimum occurrences 1
maximum occurrences 1
reference type parent_of
name Test_Performance
description The Test_Performance class contains information about a trained model's performance on the test set.
is abstract false
is choice false
subclass of USER
Associations
local identifier performance_measure
minimum occurrences 1
maximum occurrences 1
reference type attribute_of
local identifier performance_score
minimum occurrences 1
maximum occurrences 1
reference type attribute_of
name Trained_Machine_Learning_Model
description The Trained_Machine_Learning_Model class is a container for information about how a given model was trained and evaluated. A Machine_Learning_Algorithm and Training_Set are required, while Validation_Set and Test_Set (and Test_Performance) are optional.
is abstract false
is choice false
subclass of USER
Associations
local identifier trained_model_version_id
minimum occurrences 1
maximum occurrences 1
reference type attribute_of
local identifier trained_model_name
minimum occurrences 1
maximum occurrences 1
reference type attribute_of
local identifier Machine_Learning_Algorithm
minimum occurrences 1
maximum occurrences 1
reference type component_of
local identifier Training_Set
minimum occurrences 1
maximum occurrences 1
reference type component_of
local identifier Validation_Set
minimum occurrences 0
maximum occurrences 1
reference type component_of
local identifier Test_Set
minimum occurrences 0
maximum occurrences 1
reference type component_of
local identifier Test_Performance
minimum occurrences 0
maximum occurrences *
reference type component_of
name Machine_Learning_Algorithm
description The Machine_Learning_Algorithm class is a container for classes and and attributes describing the algorithm type and learning style used. An external reference to a citation for the algorithm is required.
is abstract false
is choice false
subclass of USER
Associations
local identifier algorithm_learning_style
minimum occurrences 1
maximum occurrences 1
reference type attribute_of
local identifier algorithm_type
minimum occurrences 1
maximum occurrences 1
reference type attribute_of
local identifier algorithm_name
minimum occurrences 1
maximum occurrences 1
reference type attribute_of
local identifier pds.External_Reference
minimum occurrences 1
maximum occurrences *
reference type component_of
name Machine_Learning
description The Machine_Learning class is a container for all machine learning information in the label.
is abstract false
is choice false
subclass of USER
Associations
local identifier Trained_Machine_Learning_Model
minimum occurrences 1
maximum occurrences *
reference type component_of
End of Report
PK YUm`h h PDS4_ML_1J00_1100.sch
Schematron using XPath 2.0
ml:Machine_Learning_Algorithm/ml:algorithm_learning_style/ml:algorithm_learning_style
The attribute ml:Machine_Learning_Algorithm/ml:algorithm_learning_style must be equal to one of the following values 'Semisupervised_Learning', 'Supervised_Learning', 'Unsupervised_Learning'.
ml:Machine_Learning_Algorithm/ml:algorithm_type/ml:algorithm_type
The attribute ml:Machine_Learning_Algorithm/ml:algorithm_type must be equal to the value 'Convolutional_Neural_Network_Classifier'.
PK YUSհ# # PDS4_ML_1J00_1100.csv"Sort Key","Type","Name","Version","Name Space Id","Description","Steward","Value Type","Minimum Cardinality","Maximum Cardinality","Minimum Value","Maximum Value","Minimum Characters","Maximum Characters","Unit of Measure Type","Specified Unit Id","Attribute Concept","Conceptual Domain"
"ml:Data_Set:1 ","Class","Data_Set","1.0.0.0","ml","The Data_Set class is the container for classes and attributes describing the size and version of data sets used by the machine learning model.","img","","","","","","","","","","",""
"ml:Data_Set:2 ml:data_set_version_id:1 ","Attribute","data_set_version_id","n/a","ml","The data_set_version_id attribute specifies the data set version number.","img","ASCII_VID","1","1","Unbounded","Unbounded","3","100","Units_of_None","none","TBD_classConcept","SHORT_STRING"
"ml:Data_Set:2 ml:data_set_count:1 ","Attribute","data_set_count","n/a","ml","The data_set_count attribute provides the number of items in the data set.","img","ASCII_NonNegative_Integer","1","1","1","18446744073709551615","Unbounded","Unbounded","Units_of_None","none","TBD_classConcept","INTEGER"
"ml:Training_Set:1 ","Class","Training_Set","1.0.0.0","ml","The Training_Set class belongs to the Data_Set class family and contains attributes that describe the size and version of the data set used to train the machine learning model.","img","","","","","","","","","","",""
"ml:Training_Set:2 ml:data_set_version_id:1 ","Attribute","data_set_version_id","n/a","ml","The data_set_version_id attribute specifies the data set version number.","img","ASCII_VID","1","1","Unbounded","Unbounded","3","100","Units_of_None","none","TBD_classConcept","SHORT_STRING"
"ml:Training_Set:2 ml:data_set_count:1 ","Attribute","data_set_count","n/a","ml","The data_set_count attribute provides the number of items in the data set.","img","ASCII_NonNegative_Integer","1","1","1","18446744073709551615","Unbounded","Unbounded","Units_of_None","none","TBD_classConcept","INTEGER"
"ml:Validation_Set:1 ","Class","Validation_Set","1.0.0.0","ml","The Validation_Set class belongs to the Data_Set class family and contains attributes that describe the size and version of the data set used to validate the machine learning model (e.g., to choose the best hyperparameters).","img","","","","","","","","","","",""
"ml:Validation_Set:2 ml:data_set_version_id:1 ","Attribute","data_set_version_id","n/a","ml","The data_set_version_id attribute specifies the data set version number.","img","ASCII_VID","1","1","Unbounded","Unbounded","3","100","Units_of_None","none","TBD_classConcept","SHORT_STRING"
"ml:Validation_Set:2 ml:data_set_count:1 ","Attribute","data_set_count","n/a","ml","The data_set_count attribute provides the number of items in the data set.","img","ASCII_NonNegative_Integer","1","1","1","18446744073709551615","Unbounded","Unbounded","Units_of_None","none","TBD_classConcept","INTEGER"
"ml:Test_Set:1 ","Class","Test_Set","1.0.0.0","ml","The Test_Set class belongs to the Data_Set class family and contains attributes describing the size and version of the data set used to test the machine learning model (i.e., in terms of generalization to previously unseen data).","img","","","","","","","","","","",""
"ml:Test_Set:2 ml:data_set_version_id:1 ","Attribute","data_set_version_id","n/a","ml","The data_set_version_id attribute specifies the data set version number.","img","ASCII_VID","1","1","Unbounded","Unbounded","3","100","Units_of_None","none","TBD_classConcept","SHORT_STRING"
"ml:Test_Set:2 ml:data_set_count:1 ","Attribute","data_set_count","n/a","ml","The data_set_count attribute provides the number of items in the data set.","img","ASCII_NonNegative_Integer","1","1","1","18446744073709551615","Unbounded","Unbounded","Units_of_None","none","TBD_classConcept","INTEGER"
"ml:Test_Performance:1 ","Class","Test_Performance","1.0.0.0","ml","The Test_Performance class contains information about a trained model's performance on the test set.","img","","","","","","","","","","",""
"ml:Test_Performance:2 ml:performance_measure:1 ","Attribute","performance_measure","n/a","ml","The performance_measure attribute specifies the name of the measure (or metric) used to report performance of the model on the test set.","img","ASCII_Short_String_Collapsed","1","1","Unbounded","Unbounded","1","255","Units_of_None","none","TBD_classConcept","SHORT_STRING"
"ml:Test_Performance:2 ml:performance_score:1 ","Attribute","performance_score","n/a","ml","The performance_score attribute reports the numeric score the model achieved using performance_measure on the test set. Values are not constrained since the measure may not be a strict metric. Examples could include accuracy, loss, runtime, memory consumption, etc.","img","ASCII_Real","1","1","-1.7976931348623157e308","1.7976931348623157e308","Unbounded","Unbounded","Units_of_None","none","TBD_classConcept","REAL"
"ml:Trained_Machine_Learning_Model:1","Class","Trained_Machine_Learning_Model","1.0.0.0","ml","The Trained_Machine_Learning_Model class is a container for information about how a given model was trained and evaluated. A Machine_Learning_Algorithm and Training_Set are required, while Validation_Set and Test_Set (and Test_Performance) are optional.","img","","","","","","","","","","",""
"ml:Trained_Machine_Learning_Model:2 ml:trained_model_version_id:1 ","Attribute","trained_model_version_id","n/a","ml","The trained_model_version_id attribute specifies the trained model version number.","img","ASCII_VID","1","1","Unbounded","Unbounded","3","100","Units_of_None","none","TBD_classConcept","SHORT_STRING"
"ml:Trained_Machine_Learning_Model:2 ml:trained_model_name:1 ","Attribute","trained_model_name","n/a","ml","The trained_model_name attribute specifies the name of the model used.","img","ASCII_Short_String_Collapsed","1","1","Unbounded","Unbounded","1","255","Units_of_None","none","TBD_classConcept","SHORT_STRING"
"ml:Machine_Learning_Algorithm:1 ","Class","Machine_Learning_Algorithm","1.0.0.0","ml","The Machine_Learning_Algorithm class is a container for classes and and attributes describing the algorithm type and learning style used. An external reference to a citation for the algorithm is required.","img","","","","","","","","","","",""
"ml:Machine_Learning_Algorithm:2 ml:algorithm_learning_style:1 ","Attribute","algorithm_learning_style","n/a","ml","The algorithm_learning_style attribute describes the type of learning style employed by the algorithm to solve a problem. Specifically, the learning style depends on whether labeled or unlabeled data was employed to train the model. Labeled data includes observations that are associated with a desired output such as a class or numeric value.","img","ASCII_Short_String_Collapsed","1","1","Unbounded","Unbounded","1","255","Units_of_None","none","TBD_classConcept","SHORT_STRING"
"ml:Machine_Learning_Algorithm:2 ml:algorithm_learning_style:2 Value:Semisupervised_Learn","Value","Semisupervised_Learning","","","Both labeled data and unlabeled data were used to inform the model."
"ml:Machine_Learning_Algorithm:2 ml:algorithm_learning_style:2 Value:Supervised_Learning","Value","Supervised_Learning","","","Supervised_Learning indicates that labeled data has been used to train a model to yield the desired output."
"ml:Machine_Learning_Algorithm:2 ml:algorithm_learning_style:2 Value:Unsupervised_Learnin","Value","Unsupervised_Learning","","","The algorithm did not employ any labeled data and instead discovered patterns from unlabeled data."
"ml:Machine_Learning_Algorithm:2 ml:algorithm_type:1 ","Attribute","algorithm_type","n/a","ml","The algorithm_type attribute describes the kind of algorithm used, such as a regression model, neural network, tree, etc.","img","ASCII_Short_String_Collapsed","1","1","Unbounded","Unbounded","1","255","Units_of_None","none","TBD_classConcept","SHORT_STRING"
"ml:Machine_Learning_Algorithm:2 ml:algorithm_type:2 Value:Convolutional_Neural","Value","Convolutional_Neural_Network_Classifier","","","The algorithm used for data analysis is a Convolutional Neural Network (CNN) Classifier."
"ml:Machine_Learning_Algorithm:2 ml:algorithm_name:1 ","Attribute","algorithm_name","n/a","ml","The algorithm_name attribute specifies the name of the algorithm used.","img","ASCII_Short_String_Collapsed","1","1","Unbounded","Unbounded","1","255","Units_of_None","none","TBD_classConcept","SHORT_STRING"
"ml:Machine_Learning:1 ","Class","Machine_Learning","1.0.0.0","ml","The Machine_Learning class is a container for all machine learning information in the label. ","img","","","","","","","","","","",""
PK YU3z
PDS4_ML_1J00_1100.xml
urn:nasa:pds:system_bundle:xml_schema:ml-xml_schema_1.19.0.0_1.1.0.0
1.25
PDS4 XML Schema - ML V1.1.0.0
1.19.0.0
Product_XML_Schema
2022-10-20
1.25
This is the system generated PDS4 product label for PDS4 XML Schema and Schematron files.
PDS4_ML_1J00_1100.xsd
2022-10-20T14:52:41
12912
293
PDS4_ML_1J00_1100.xsd
0
XML Schema Version 1.1
This is a PDS4 XML Schema file for the declared namespace.
PDS4_ML_1J00_1100.sch
2022-10-20T14:52:41
2152
35
PDS4_ML_1J00_1100.sch
0
Schematron ISO/IEC 19757-3:2006
This is the PDS4 Schematron file for the declared namespace. Schematron provides rule-based validation for XML Schema.
PK YUID D PDS4_ML_IngestLDD.xml
Machine Learning Analysis
1.1.0.0
Discipline
Sara A. Bond
img
ml
This namespace enables the specification of metadata that
describes products generated by the use of a machine learning
model. It captures information about how the model was trained
and evaluated.
## CHANGE LOG ##
1.1.0.0
- Change data_set_size to data_set_count.
- Expand attribute definitions.
- Update steward name.
1.0.1.0
- Address Oxygen-flagged errors.
- Make data_set_size a ASCII_NonNegative_Integer.
- Update some attribute and class definitions.
- Update README file per latest template.
- Use PDS Information Model 1.18 (I).
1.0.0.0
- Initial release.
2022-06-30
data_set_count
1.1
data_set_count
false
Minh Le
The data_set_count attribute provides the number of
items in the data set.
false
ASCII_NonNegative_Integer
1
Units_of_None
data_set_version_id
1.0
data_set_version_id
false
Minh Le
The data_set_version_id attribute specifies the data
set version number.
false
ASCII_VID
Units_of_None
algorithm_learning_style
1.0
algorithm_learning_style
false
Minh Le
The algorithm_learning_style attribute describes the
type of learning style employed by the algorithm to solve a
problem. Specifically, the learning style depends on whether
labeled or unlabeled data was employed to train the model.
Labeled data includes observations that are associated with a
desired output such as a class or numeric value.
true
ASCII_Short_String_Collapsed
Supervised_Learning
Supervised_Learning indicates that labeled data
has been used to train a model to yield the desired
output.
Semisupervised_Learning
Both labeled data and unlabeled data were used
to inform the model.
Unsupervised_Learning
The algorithm did not employ any labeled data
and instead discovered patterns from unlabeled
data.
algorithm_type
1.0
algorithm_type
false
Minh Le
The algorithm_type attribute describes the kind of
algorithm used, such as a regression model, neural network, tree,
etc.
We plan to expand the list of permissible values.
true
ASCII_Short_String_Collapsed
Convolutional_Neural_Network_Classifier
The algorithm used for data analysis is a
Convolutional Neural Network (CNN) Classifier.
algorithm_name
1.1
algorithm_name
false
Minh Le
The algorithm_name attribute specifies the name of the
algorithm used.
false
ASCII_Short_String_Collapsed
performance_measure
1.0
performance_measure
false
Kiri L. Wagstaff
The performance_measure attribute specifies the name
of the measure (or metric) used to report performance of the
model on the test set.
false
ASCII_Short_String_Collapsed
performance_score
1.0
performance_score
false
Kiri L. Wagstaff
The performance_score attribute reports the numeric
score the model achieved using performance_measure on the test
set. Values are not constrained since the measure may not be a
strict metric. Examples could include accuracy, loss, runtime,
memory consumption, etc.
false
ASCII_Real
Units_of_None
trained_model_version_id
1.0
trained_model_version_id
false
Kiri L. Wagstaff
The trained_model_version_id attribute specifies the
trained model version number.
false
ASCII_VID
Units_of_None
trained_model_name
1.0
trained_model_name
false
Kiri L. Wagstaff
The trained_model_name attribute specifies the name of the
model used.
false
ASCII_Short_String_Collapsed
Data_Set
1.0
Data_Set
Minh Le
The Data_Set class is the container for classes
and attributes describing the size and version of data sets used
by the machine learning model.
true
false
data_set_version_id
attribute_of
1
1
data_set_count
attribute_of
1
1
Training_Set
1.0
Training_Set
Minh Le
The Training_Set class belongs to the Data_Set
class family and contains attributes that describe the
size and version of the data set used to train the machine
learning model.
false
Data_Set
parent_of
1
1
Validation_Set
1.0
Validation_Set
Minh Le
The Validation_Set class belongs to the Data_Set
class family and contains attributes that describe the
size and version of the data set used to validate the machine
learning model (e.g., to choose the best
hyperparameters).
false
Data_Set
parent_of
1
1
Test_Set
1.0
Test_Set
Minh Le
The Test_Set class belongs to the Data_Set class
family and contains attributes describing the size and version of
the data set used to test the machine learning model (i.e., in
terms of generalization to previously unseen data).
false
Data_Set
parent_of
1
1
Test_Performance
1.0
Test_Performance
Kiri L. Wagstaff
The Test_Performance class contains information about
a trained model's performance on the test set.
false
performance_measure
attribute_of
1
1
performance_score
attribute_of
1
1
Trained_Machine_Learning_Model
1.0
Trained_Machine_Learning_Model
Minh Le
The Trained_Machine_Learning_Model class is a
container for information about how a given model was trained and
evaluated. A Machine_Learning_Algorithm and Training_Set are
required, while Validation_Set and Test_Set (and Test_Performance)
are optional.
false
trained_model_version_id
attribute_of
1
1
trained_model_name
attribute_of
1
1
Machine_Learning_Algorithm
component_of
1
1
Training_Set
component_of
1
1
Validation_Set
component_of
0
1
Test_Set
component_of
0
1
Test_Performance
component_of
0
*
Machine_Learning_Algorithm
1.0
Machine_Learning_Algorithm
Minh Le
The Machine_Learning_Algorithm class is a container
for classes and and attributes describing the algorithm type and
learning style used. An external reference to a citation for the
algorithm is required.
false
algorithm_learning_style
attribute_of
1
1
algorithm_type
attribute_of
1
1
algorithm_name
attribute_of
1
1
pds.External_Reference
component_of
1
*
Machine_Learning
1.0
Machine_Learning
Kiri L. Wagstaff
The Machine_Learning class is a container for all
machine learning information in the label.
false
true
Trained_Machine_Learning_Model
component_of
1
*
PK YUaw'Ky y PDS4_ML_1J00_1100.JSONPK YU]p2 p2 y PDS4_ML_1J00_1100.xsdPK YU\) \) y PDS4_ML_1J00_1100.txtPK YUm`h h PDS4_ML_1J00_1100.schPK YUSհ# # PDS4_ML_1J00_1100.csvPK YU3z
PDS4_ML_1J00_1100.xmlPK YUID D
PDS4_ML_IngestLDD.xmlPK Q