com.alpine.model.pack.multiple
PipelineClassificationModel
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package multiple
The total set of classes that the predicted value can take on.
The total set of classes that the predicted value can take on.
The set of possible values for the predicted value.
This should return a representative class from every jar that is needed to load the object during deserialization.
This should return a representative class from every jar that is needed to load the object during deserialization.
Default implementation returns the class of MLModel, and the class of the model implementation.
Used when we are doing model quality evaluation e.g.
Used when we are doing model quality evaluation e.g. Confusion Matrix, so we know what feature in the test dataset to compare the result to.
Feature description used to identify the dependent feature in an evaluation dataset.
Used to identify this model when in a collection of models.
Used to identify this model when in a collection of models. Should be simple characters, so it can be used in a feature name.
identifier for the model.
Returns a streamlined version of this model, throwing away parts which are not required to produce the features with names in requiredOutputFeatureNames.
Returns a streamlined version of this model, throwing away parts which are not required to produce the features with names in requiredOutputFeatureNames.
Behaviour is undefined if requiredOutputFeatureNames is not actually a subset of outputFeatures.map(_columnName).
The resulting model may still have more output features than those required (i.e. for ClassificationRowModel implementations which always have the same output features, or for OneHotEncodingModel whose output features naturally group together).
names of the output features that the new model should produce.
A model which has the same functionality for those output features listed, but potentially fewer input features.
Used for combining a Classification model (e.g. Logistic Regression) with preprocessors (e.g. One Hot Encoding).