case class RotatedFeatureModel[T](baseModel: Model[PredictionResult[T]], rotatedFeatures: IndexedSeq[Int], trans: DenseMatrix[Double]) extends Model[PredictionResult[T]] with Product with Serializable
Model bundling the base learner's model with the list of rotated features and the transformation
- T
label type
- baseModel
model to which to delegate prediction on rotated features
- rotatedFeatures
indices of features to rotate
- trans
matrix to apply to features
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- new RotatedFeatureModel(baseModel: Model[PredictionResult[T]], rotatedFeatures: IndexedSeq[Int], trans: DenseMatrix[Double])
- baseModel
model to which to delegate prediction on rotated features
- rotatedFeatures
indices of features to rotate
- trans
matrix to apply to features
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- val baseModel: Model[PredictionResult[T]]
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- def productElementNames: Iterator[String]
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- Product
- val rotatedFeatures: IndexedSeq[Int]
- 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
- final def synchronized[T0](arg0: => T0): T0
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- val trans: DenseMatrix[Double]
- def transform(inputs: Seq[Vector[Any]]): RotatedFeaturePrediction[T]
Transform the inputs and then apply the base model
Transform the inputs and then apply the base model
- inputs
to apply the model to
- returns
a RotatedFeaturePredictionResult which includes, at least, the expected outputs
- Definition Classes
- RotatedFeatureModel → Model
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