object RegressionTree_GB extends ModelFactory
The RegressionTree_GB companion object defines hyper-parameters and provides
a factory function.
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- def allForms(x: MatriD): MatriD
Create all forms/terms for each row/point placing them in a new matrix.
Create all forms/terms for each row/point placing them in a new matrix.
- x
the original un-expanded input/data matrix
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- ModelFactory
- def apply(x: MatriD, y: VectoD, fname: Strings, hparam: HyperParameter): RegressionTree_GB
Create a
RegressionTree_GBobject that uses Gradient Boosting onRegressionTree.Create a
RegressionTree_GBobject that uses Gradient Boosting onRegressionTree. One Tree is included in the model at a time wisely chosen for reducing gradient.- x
the data matrix
- y
the response vector
- hparam
the hyper-parameters for the model
- def apply(xy: MatriD, fname: Strings = null, hparam: HyperParameter = hp): RegressionTree_GB
Create a
RegressionTree_GBobject that uses Gradient Boosting onRegressionTree.Create a
RegressionTree_GBobject that uses Gradient Boosting onRegressionTree. One Tree is included in the model at a time wisely chosen for reducing gradient.- xy
the combined data-response matrix
- hparam
the hyper-parameters for the model
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- val drp: (Null, HyperParameter)
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- def forms(xi: VectoD, k: Int, nt: Int): VectoD
Given a vector/point 'v', compute the values for all of its forms/terms, returning them as a vector (assumes
Regressionwith intercept).Given a vector/point 'v', compute the values for all of its forms/terms, returning them as a vector (assumes
Regressionwith intercept). Override for expanded columns, e.g.,QuadRegression.- xi
the vector/point (i-th row of x) for creating forms/terms
- k
the number of features/predictor variables (not counting intercept)
- nt
the number of terms
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- val hp: HyperParameter
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- def numTerms(k: Int): Int
The number of terms/parameters in the model (assumes
Regressionwith intercept.The number of terms/parameters in the model (assumes
Regressionwith intercept. Override for expanded columns, e.g.,QuadRegression.- k
the number of features/predictor variables (not counting intercept)
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- val rescale: Boolean
The 'rescale' flag indicated whether the data is to be rescaled/normalized
The 'rescale' flag indicated whether the data is to be rescaled/normalized
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- def rescaleOff(): Unit
Turn rescaling off.
Turn rescaling off.
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- def rescaleOn(): Unit
Turn rescaling on.
Turn rescaling on.
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