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.
The average number of items contained in a transaction.
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.
The maximum number of items contained in a rule which was used to limit the number of rules.
The number of items contained in the largest transaction.
The minimum confidence value satisfied by all rules.
The minimum confidence value satisfied by all rules. Confidence is calculated as (support (rule) / support(antecedent)).
The minimum relative support value (#supporting transactions / #total transactions) satisfied by all rules.
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.
The number of different items contained in the input data.
The number of different items contained in the input data. This number may be greater than or equal to the number of items contained in the model. The value will be greater if any items in the input data are excluded from the model, as a consequence of not being referenced by the model.
The number of itemsets contained in the model.
The number of rules contained in the model.
The number of transactions contained in the input data.
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.