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.
An informational string describing the technique used by the model designer to establish the baseline scores.
A single value to use as the baseline comparison score for all characteristics, when determining reason code ranking.
A single value to use as the baseline comparison score for all characteristics, when determining reason code ranking. Alternatively, unique baseline scores may be set for each individual Characteristic as shown below. This attribute is required only when useReasonCodes is "true" and attribute baselineScore is not given for each Characteristic.
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.
Initial score contains a value which is added to the overall score whenever partial scores are summed up.
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.
May be "pointsAbove" or "pointsBelow", describing how reason codes shall be ranked, relative to the baseline score of each Characteristic, or as set at the top-level scorecard.
If "false", reason codes are not computed as part of the scorecard.
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.