Description Usage Arguments Details Value

Computes the smallest value of the LASSO coefficient L1 that leads to an all-zero weight vector for a given linear regression problem.

1 2 |

`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. |

`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 |

`l2` |
coefficient for the L2-norm penalty |

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

The cyclic coordinate descent updates the model weight *w_k* using a soft threshold operator
* S( \cdot, λ_1 d_k ) * that clips the value of the weight to zero, whenever the absolute
value of the first argument falls below *λ_1 d_k*. From here, it is straightforward to compute
the smallest value of *λ_1*, such that all weights are clipped to zero.

The largest meaningful value of the L1 parameter (i.e., the smallest value that yields a model with all zero weights)

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