AnomalyDetectionAttributes
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package 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.
Defines a model type used by the anomaly model.
Defines a model type used by the anomaly model.
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.
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.
A required parameter for isolation forest models.
A required parameter for isolation forest models. It is the dataset size used to train the forest and is needed to normalize the tree search depth.
Holds attributes of an Anomaly Detection Model.