class BaggedModel[+T] extends Model[BaggedResult[T]]
Container holding a parallel sequence of models and the sample counts used to train them
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- new BaggedModel(models: ParSeq[Model[PredictionResult[T]]], Nib: Vector[Vector[Int]], useJackknife: Boolean, biasModel: Option[Model[PredictionResult[T]]] = None, rescale: Double = 1.0, disableBootstrap: Boolean = false)(implicit arg0: ClassTag[T])
- models
in this bagged model
- Nib
training sample counts
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- def shapley(input: Vector[Any], omitFeatures: Set[Int] = Set()): Option[DenseMatrix[Double]]
Compute Shapley feature attributions for a given input
Compute Shapley feature attributions for a given input
- 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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- BaggedModel → Model
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- def transform(inputs: Seq[Vector[Any]]): BaggedResult[T]
Apply each model to the outputs and wrap them up
Apply each model to the outputs and wrap them up
- inputs
to apply the model to
- returns
a predictionresult that includes uncertainties and scores
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- BaggedModel → Model
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