BTFit {BT} | R Documentation |

These are objects representing fitted boosting trees.

Boosting Tree Model Object.

`BTInit` |
an object of class |

`BTErrors` |
an object of class |

`BTIndivFits` |
an object of class |

`distribution` |
the Tweedie power (and so the distribution) that has been used to perform the algorithm. It will currently always output 1. |

`var.names` |
a vector containing the names of the explanatory variables. |

`response` |
the name of the target/response variable. |

`w` |
a vector containing the weights used. |

`seed` |
the used seed, if any. |

`BTData` |
if |

`BTParams` |
an object of class |

`keep.data` |
the |

`is.verbose` |
the |

`fitted.values` |
the training set fitted values on the score scale using all the |

`cv.folds` |
the number of cross-validation folds. Set to 1 if no cross-validation performed. |

`call` |
the original call to the |

`Terms` |
the |

`folds` |
a vector of values identifying to which fold each observation is in. This argument is not present if there is no cross-validation. On the other hand, it corresponds
to |

`cv.fitted` |
a vector containing the cross-validation fitted values, if a cross-validation was performed. More precisely, for a given observation, the prediction will be furnished by the cv-model
for which this specific observation was out-of-fold. See |

The following components must be included in a legitimate `BTFit`

object.

Gireg Willame gireg.willame@gmail.com

*This package is inspired by the gbm3 package. For more details, see https://github.com/gbm-developers/gbm3/*.

M. Denuit, D. Hainaut and J. Trufin (2019). **Effective Statistical Learning Methods for Actuaries |: GLMs and Extensions**, *Springer Actuarial*.

M. Denuit, D. Hainaut and J. Trufin (2019). **Effective Statistical Learning Methods for Actuaries ||: Tree-Based Methods and Extensions**, *Springer Actuarial*.

M. Denuit, D. Hainaut and J. Trufin (2019). **Effective Statistical Learning Methods for Actuaries |||: Neural Networks and Extensions**, *Springer Actuarial*.

M. Denuit, D. Hainaut and J. Trufin (2022). **Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link**.
Accepted for publication in *Scandinavian Actuarial Journal*.

M. Denuit, J. Huyghe and J. Trufin (2022). **Boosting cost-complexity pruned trees on Tweedie responses: The ABT machine for insurance ratemaking**.
Paper submitted for publication.

M. Denuit, J. Trufin and T. Verdebout (2022). **Boosting on the responses with Tweedie loss functions**. Paper submitted for publication.

`BT`

.

[Package *BT* version 0.4 Index]