These are objects representing fitted `gbm`

s.

`initF` |
the "intercept" term, the initial predicted value to which trees make adjustments |

`fit` |
a vector containing the fitted values on the scale of regression function (e.g. log-odds scale for bernoulli, log scale for poisson) |

`train.error` |
a vector of length equal to the number of fitted trees containing the value of the loss function for each boosting iteration evaluated on the training data |

`valid.error` |
a vector of length equal to the number of fitted trees containing the value of the loss function for each boosting iteration evaluated on the validation data |

`cv.error` |
if |

`oobag.improve` |
a vector of length equal to the number of fitted trees
containing an out-of-bag estimate of the marginal reduction in the expected
value of the loss function. The out-of-bag estimate uses only the training
data and is useful for estimating the optimal number of boosting iterations.
See |

`trees` |
a list containing the tree structures. The components are best
viewed using |

`c.splits` |
a list of all the categorical splits in the collection of
trees. If the |

`cv.fitted` |
If cross-validation was performed, the cross-validation predicted values on the scale of the linear predictor. That is, the fitted values from the ith CV-fold, for the model having been trained on the data in all other folds. |

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

object.

Greg Ridgeway gregridgeway@gmail.com

`gbm`

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.