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 ## 1.0.1.0 - 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 machine learning model test set. 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 machine learning model training set. The Validation_Set class belongs to the Data_Set class family and contains attributes that describe the size and version of the machine learning model validation set. 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. 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_size 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. The trained_model_name attribute specifies the name of the model used. The trained_model_version_id attribute specifies the trained model version number.