gelnet.logreg.obj
(Package: gelnet) :
Logistic regression objective function value
Evaluates the logistic regression objective function value for a given model. See details. Computes the objective function value according to
● Data Source:
CranContrib
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● Alias: gelnet.logreg.obj
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gelnet.cv
(Package: gelnet) :
k-fold cross-validation for parameter tuning.
Performs k-fold cross-validation to select the best pair of the L1- and L2-norm penalty values.
● Data Source:
CranContrib
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● Alias: gelnet.cv
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gelnet.oneclass.obj
(Package: gelnet) :
One-class regression objective function value
Evaluates the one-class objective function value for a given model See details.
● Data Source:
CranContrib
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● Alias: gelnet.oneclass.obj
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L1.ceiling
(Package: gelnet) :
The largest meaningful value of the L1 parameter
Computes the smallest value of the LASSO coefficient L1 that leads to an all-zero weight vector for a given linear regression problem.
● Data Source:
CranContrib
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● Alias: L1.ceiling
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adj2nlapl
(Package: gelnet) :
Generate a normalized graph Laplacian
Generates a normalized graph Laplacian from the graph adjacency matrix.
● Data Source:
CranContrib
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● Alias: adj2nlapl
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gelnet.ker
(Package: gelnet) :
Kernel models for linear regression, binary classification and one-class problems.
Infers the problem type and learns the appropriate kernel model.
● Data Source:
CranContrib
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● Alias: gelnet.ker
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gelnet.lin.obj
(Package: gelnet) :
Linear regression objective function value
Evaluates the linear regression objective function value for a given model. See details.
● Data Source:
CranContrib
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● Alias: gelnet.lin.obj
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adj2lapl
(Package: gelnet) :
Generate a graph Laplacian
Generates a graph Laplacian from the graph adjacency matrix.
● Data Source:
CranContrib
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● Alias: adj2lapl
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gelnet
(Package: gelnet) :
GELnet for linear regression, binary classification and one-class problems.
Infers the problem type and learns the appropriate GELnet model via coordinate descent.
● Data Source:
CranContrib
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● Alias: gelnet
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