bst_control {bst} | R Documentation |

Specification of the number of boosting iterations, step size and other parameters for boosting algorithms.

bst_control(mstop = 50, nu = 0.1, twinboost = FALSE, twintype=1, threshold=c("standard", "adaptive"), f.init = NULL, coefir = NULL, xselect.init = NULL, center = FALSE, trace = FALSE, numsample = 50, df = 4, s = NULL, sh = NULL, q = NULL, qh = NULL, fk = NULL, start=FALSE, iter = 10, intercept = FALSE, trun=FALSE)

`mstop` |
an integer giving the number of boosting iterations. |

`nu` |
a small number (between 0 and 1) defining the step size or shrinkage parameter. |

`twinboost` |
a logical value: |

`twintype` |
for |

`threshold` |
if |

`f.init` |
the estimate from the first round of twin boosting. Only useful when |

`coefir` |
the estimated coefficients from the first round of twin boosting. Only useful when |

`xselect.init` |
the variable selected from the first round of twin boosting. Only useful when |

`center` |
a logical value: |

`trace` |
a logical value for printout of more details of information during the fitting process. |

`numsample` |
number of random sample variable selected in the first round of twin boosting. This is potentially useful in the future implementation. |

`df` |
degree of freedom used in smoothing splines. |

`s,q` |
nonconvex loss tuning parameter |

`sh, qh` |
threshold value or frequency |

`fk` |
predicted values at an iteration in the MM algorithm |

`start` |
a logical value, if |

`iter` |
number of iteration in the MM algorithm |

`intercept` |
logical value, if TRUE, estimation of intercept with linear predictor model |

`trun` |
logical value, if TRUE, predicted value in each boosting iteration is truncated at -1, 1, for |

Objects to specify parameters of the boosting algorithms implemented in `bst`

, via the `ctrl`

argument.
The `s`

value is for robust nonconvex loss where smaller `s`

value is more robust to outliers with `family="closs", "tbinom", "thinge", "tbinomd"`

, and larger `s`

value more robust with `family="clossR", "gloss", "qloss"`

.

For `family="closs"`

, if `s=2`

, the loss is similar to the square loss; if `s=1`

, the loss function is an approximation of the hinge loss; for smaller values, the loss function approaches the 0-1 loss function if `s<1`

, the loss function is a nonconvex function of the margin.

The default value of `s`

is -1 if `family="thinge"`

, -log(3) if `family="tbinom"`

, and 4 if `family="binomd"`

. If `trun=TRUE`

, boosting classifiers can produce real values in [-1, 1] indicating their confidence in [-1, 1]-valued classification. cf. R. E. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pages 80-91, 1998.

An object of class `bst_control`

, a list. Note `fk`

may be updated for robust boosting.

[Package *bst* version 0.3-23 Index]