class Target extends PmmlElement
Note that castInteger, min, max, rescaleConstant and rescaleFactor only apply to models of type regression. Furthermore, they must be applied in sequence, which is:
min and max rescaleFactor rescaleConstant castInteger
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- new Target(field: Option[String], optype: Option[OpType], castInteger: Option[CastInteger], min: Option[Double], max: Option[Double], rescaleConstant: Double = 0.0, rescaleFactor: Double = 1.0, targetValues: Array[TargetValue] = Array.empty)
- field
must refer to a name of a DataField or DerivedField. It can be absent when the model is used inside a Segment of a MiningModel and does not have a real target field in the input data
- optype
When Target specifies optype then it overrides the optype attribute in a corresponding MiningField, if it exists. If the target does not specify optype then the MiningField is used as default. And, in turn, if the MiningField does not specify an optype, it is taken from the corresponding DataField. In other words, a MiningField overrides a DataField, and a Target overrides a MiningField.
- castInteger
If a regression model should predict integers, use the attribute castInteger to control how decimal places should be handled.
- min
If min is present, the predicted value will be the value of min if it is smaller than that.
- max
If max is present, the predicted value will be max if it is larger than that.
- rescaleConstant
can be used for simple rescale of the predicted value: First off, the predicted value is multiplied by rescaleFactor.
- rescaleFactor
after that, rescaleConstant is added to the predicted value.
- targetValues
In classification models, TargetValue is required. For regression models, TargetValue is only optional.
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- val optype: Option[OpType]
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(Since version 9)