blackboost | R Documentation |

Gradient boosting for optimizing arbitrary loss functions where regression trees are utilized as base-learners.

blackboost(formula, data = list(), weights = NULL, na.action = na.pass, offset = NULL, family = Gaussian(), control = boost_control(), oobweights = NULL, tree_controls = partykit::ctree_control( teststat = "quad", testtype = "Teststatistic", mincriterion = 0, minsplit = 10, minbucket = 4, maxdepth = 2, saveinfo = FALSE), ...)

`formula` |
a symbolic description of the model to be fit. |

`data` |
a data frame containing the variables in the model. |

`weights` |
an optional vector of weights to be used in the fitting process. |

`na.action` |
a function which indicates what should happen when the data
contain |

`offset` |
a numeric vector to be used as offset (optional). |

`family` |
a |

`control` |
a list of parameters controlling the algorithm. For
more details see |

`oobweights` |
an additional vector of out-of-bag weights, which is
used for the out-of-bag risk (i.e., if |

`tree_controls` |
an object of class |

`...` |
additional arguments passed to |

This function implements the ‘classical’
gradient boosting utilizing regression trees as base-learners.
Essentially, the same algorithm is implemented in package
`gbm`

. The
main difference is that arbitrary loss functions to be optimized
can be specified via the `family`

argument to `blackboost`

whereas
`gbm`

uses hard-coded loss functions.
Moreover, the base-learners (conditional
inference trees, see `ctree`

) are a little bit more flexible.

The regression fit is a black box prediction machine and thus hardly interpretable.

Partial dependency plots are not yet available; see example section for plotting of additive tree models.

An object of class `mboost`

with `print`

and `predict`

methods being available.

Peter Buehlmann and Torsten Hothorn (2007),
Boosting algorithms: regularization, prediction and model fitting.
*Statistical Science*, **22**(4), 477–505.

Torsten Hothorn, Kurt Hornik and Achim Zeileis (2006). Unbiased recursive
partitioning: A conditional inference framework. *Journal of
Computational and Graphical Statistics*, **15**(3), 651–674.

Yoav Freund and Robert E. Schapire (1996),
Experiments with a new boosting algorithm.
In *Machine Learning: Proc. Thirteenth International Conference*,
148–156.

Jerome H. Friedman (2001),
Greedy function approximation: A gradient boosting machine.
*The Annals of Statistics*, **29**, 1189–1232.

Greg Ridgeway (1999), The state of boosting.
*Computing Science and Statistics*, **31**,
172–181.

See `mboost_fit`

for the generic boosting function,
`glmboost`

for boosted linear models, and
`gamboost`

for boosted additive models.

See `baselearners`

for possible base-learners.

See `cvrisk`

for cross-validated stopping iteration.

Furthermore see `boost_control`

, `Family`

and
`methods`

.

### a simple two-dimensional example: cars data cars.gb <- blackboost(dist ~ speed, data = cars, control = boost_control(mstop = 50)) cars.gb ### plot fit plot(dist ~ speed, data = cars) lines(cars$speed, predict(cars.gb), col = "red") ### set up and plot additive tree model if (require("partykit")) { ctrl <- ctree_control(maxdepth = 3) viris <- subset(iris, Species != "setosa") viris$Species <- viris$Species[, drop = TRUE] imod <- mboost(Species ~ btree(Sepal.Length, tree_controls = ctrl) + btree(Sepal.Width, tree_controls = ctrl) + btree(Petal.Length, tree_controls = ctrl) + btree(Petal.Width, tree_controls = ctrl), data = viris, family = Binomial())[500] layout(matrix(1:4, ncol = 2)) plot(imod) }

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