class ParallelModels extends MultiTaskModel
A set of parallel models, one for each label.
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Instance Constructors
- new ParallelModels(models: Seq[Model[PredictionResult[Any]]], realLabels: Seq[Boolean])
- models
sequence of models, one for each label
- realLabels
boolean sequence indicating which labels are real-valued
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- def getModels: Seq[Model[PredictionResult[Any]]]
Individual models corresponding to each label
Individual models corresponding to each label
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- ParallelModels → MultiTaskModel
- def getRealLabels: Seq[Boolean]
A boolean sequence indicating which labels are real-valued.
A boolean sequence indicating which labels are real-valued. Its length must be equal to
numLabels.- Definition Classes
- ParallelModels → MultiTaskModel
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- val numLabels: Int
The number of labels.
The number of labels. Every prediction must have this length.
- Definition Classes
- ParallelModels → MultiTaskModel
- 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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- Model
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- def transform(inputs: Seq[Vector[Any]]): ParallelModelsPredictionResult
Apply the model to a seq of inputs
Apply the model to a seq of inputs
- inputs
to apply the model to
- returns
a PredictionResult which includes, at least, the expected outputs
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- ParallelModels → Model
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