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.
Specify the scoring (or combining) method based on the categorical target values of K neighbors.
Specify the scoring (or combining) method based on the categorical target values of K neighbors.
Specify the scoring (or combining) method based on the continuous target values of K neighbors.
Specify the scoring (or combining) method based on the continuous target values of K neighbors.
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.
Contains the instance ID variable name and so refers to the name of a field in InstanceFields.
Contains the instance ID variable name and so refers to the name of a field in InstanceFields. Required if the model has no targets, optional otherwise.
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.
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.
Specifies K, the number of desired neighbors.
Specifies K, the number of desired neighbors.
Defines a very small positive number to be used for "weighted" scoring methods to avoid numerical problems when distance or similarity measure is zero.
Defines a very small positive number to be used for "weighted" scoring methods to avoid numerical problems when distance or similarity measure is zero.