class LinearRegressionModel extends Model[LinearRegressionResult]
Linear regression model as a coefficient vector and intercept
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- new LinearRegressionModel(beta: DenseVector[Double], intercept: Double, indices: Option[(Vector[Int], Int)] = None)
- beta
coefficient vector
- intercept
intercept
- indices
optional indices from which to extract real features
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- def getBeta(): Vector[Double]
Get the beta from the linear model \beta^T X = y
Get the beta from the linear model \beta^T X = y
- returns
beta as a vector of double
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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]]): LinearRegressionResult
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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- LinearRegressionModel → Model
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