HasWrappedTreeAttributes
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package model
Common attributes of this model
Common attributes of this model
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.g., whether it is intended to be used for clustering or for classification.
Describe the kind of mining model, e.g., whether it is intended to be used for clustering or for classification.
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.
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.
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.
Defines a penalty applied to confidence calculation when missing value handling is performed.
Defines a penalty applied to confidence calculation when missing value handling is performed.
Defines a strategy for dealing with missing values.
Defines a strategy for dealing with missing values.
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 what to do in situations where scoring cannot reach a leaf node.
Defines what to do in situations where scoring cannot reach a leaf node.
Indicates whether non-leaf Nodes in the tree model have exactly two children, or an unrestricted number of children.
Indicates whether non-leaf Nodes in the tree model have exactly two children, or an unrestricted number of children. In the case of multiSplit, it means that each Node may have 0 or more child Nodes. In the case of binarySplit, it means that each Node must have either 0 or 2 child Nodes.