class StandardizerModel[T] extends Model[PredictionResult[T]]
Model that wraps the base model next to the transformations
- T
type of prediction
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- new StandardizerModel(baseModel: Model[PredictionResult[T]], outputTrans: Option[Standardization], inputTrans: Seq[Option[Standardization]])
- baseModel
model trained on the standardized inputs and outputs
- outputTrans
optional transformation (rescale, offset) of output label
- inputTrans
sequence of optional transformations (rescale, offset) of inputs
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- def shapley(input: Vector[Any], omitFeatures: Set[Int] = Set()): Option[DenseMatrix[Double]]
Compute Shapley feature attributions for a given input in this node's subtree
Compute Shapley feature attributions for a given input in this node's subtree
- input
for which to compute feature attributions.
- omitFeatures
feature indices to omit in computing Shapley values
- returns
matrix of attributions for each feature and output One row per feature, each of length equal to the output dimension. The output dimension is 1 for single-task regression, or equal to the number of classification categories.
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- def transform(inputs: Seq[Vector[Any]]): StandardizerPrediction[T]
Standardize the inputs and then apply the base model
Standardize the inputs and then apply the base model
- inputs
to apply the model to
- returns
a predictionresult which includes, at least, the expected outputs
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- StandardizerModel → Model
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