The algorithm name is free-type and can be any description for the specific algorithm that produced the model.
The algorithm name is free-type and can be any description for the specific algorithm that produced the model. This attribute is for information only.
Describe the kind of mining model, e.
Describe the kind of mining model, e.g., whether it is intended to be used for clustering or for classification.
Indicates if the model is valid for scoring.
Indicates if the model is valid for scoring. If this attribute is true or if it is missing, then the model should be processed normally. However, if the attribute is false, then the model producer has indicated that this model is intended for information purposes only and should not be used to generate results.
Identifies the model with a unique name in the context of the PMML file.
Identifies the model with a unique name in the context of the PMML file. This attribute is not required. Consumers of PMML models are free to manage the names of the models at their discretion.
Defines which method is to be used in case of multi-class classification tasks.
Defines which method is to be used in case of multi-class classification tasks. It can be either OneAgainstAll or OneAgainstOne. This attribute is not required for binary classification or regression.
Tests if this is a association rules model.
Tests if this is a association rules model.
Tests if this is a classification model.
Tests if this is a classification model.
Tests if this is a clustering model.
Tests if this is a clustering model.
Tests if this is a mixed model.
Tests if this is a mixed model.
Tests if this is a regression model.
Tests if this is a regression model.
Tests if this is a sequences model.
Tests if this is a sequences model.
Tests if this is a time series model.
Tests if this is a time series model.
Used for classification models only.
Used for classification models only. It determines if the target category corresponding to the highest value of a Support Vector machine is the winner. Default value is false, meaning the target category with the lowest SVM value wins, consistent with previous PMML versions. This attribute also affects the comparisons with threshold value, see below for details.
Defines whether the SVM function is defined via support vectors or via the coefficients of the hyperplane for the case of linear kernel functions.
Defines a discrimination boundary to be used in case of binary classification or whenever attribute classificationMethod is defined as OneAgainstOne for multi-class classification tasks.