Description Usage Arguments Details Value See Also

Performs k-fold cross-validation to select the best pair of the L1- and L2-norm penalty values.

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`X` |
n-by-p matrix of n samples in p dimensions |

`y` |
n-by-1 vector of response values. Must be numeric vector for regression, factor with 2 levels for binary classification, or NULL for a one-class task. |

`nL1` |
number of values to consider for the L1-norm penalty |

`nL2` |
number of values to consider for the L2-norm penalty |

`nFolds` |
number of cross-validation folds (default:5) |

`a` |
n-by-1 vector of sample weights (regression only) |

`d` |
p-by-1 vector of feature weights |

`P` |
p-by-p feature association penalty matrix |

`m` |
p-by-1 vector of translation coefficients |

`max.iter` |
maximum number of iterations |

`eps` |
convergence precision |

`w.init` |
initial parameter estimate for the weights |

`b.init` |
initial parameter estimate for the bias term |

`fix.bias` |
set to TRUE to prevent the bias term from being updated (regression only) (default: FALSE) |

`silent` |
set to TRUE to suppress run-time output to stdout (default: FALSE) |

`balanced` |
boolean specifying whether the balanced model is being trained (binary classification only) (default: FALSE) |

Cross-validation is performed on a grid of parameter values. The user specifies the number of values to consider for both the L1- and the L2-norm penalties. The L1 grid values are equally spaced on [0, L1s], where L1s is the smallest meaningful value of the L1-norm penalty (i.e., where all the model weights are just barely zero). The L2 grid values are on a logarithmic scale centered on 1.

A list with the following elements:

- l1
the best value of the L1-norm penalty

- l2
the best value of the L2-norm penalty

- w
p-by-1 vector of p model weights associated with the best (l1,l2) pair.

- b
scalar, bias term for the linear model associated with the best (l1,l2) pair. (omitted for one-class models)

- perf
performance value associated with the best model. (Likelihood of data for one-class, AUC for binary classification, and -RMSE for regression)

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