Description Usage Arguments Details Value Note See Also

`predict`

classifies a new set of observations from a
previously built classifier. This function will provide either
a vector of new classes, class probability estimates, or both.

1 2 |

`object` |
object generated by |

`newdata` |
new data set to predict. This data set must be of type ‘data.frame’. Default = NULL. When default = NULL, predict produces predictions for the original training set. |

`type` |
choice for preditions. type=“class” returns the default class labels. type=“prob” returns the probability class estimates. type=“both” returns both the default class labels and probability class estimates. type=“F” returns the ensamble average. This is mainly usefull for the multiclass case. |

`n.iter` |
number of iterations to consider for the prediction. By default
this is iter from the |

`...` |
other arguments not used by this function. |

This function was modeled after `predict.rpart`

and `predict.rpart`

and `predict.C5.0`

.

`fit` |
a vector of fitted responses. Fit will be returned if type=“class”. |

`prob` |
a matrix of class probability estimates. The first column corresponds to the first label in the ‘levels’ of the response. The second column corresponds to the second label in the ‘levels’ of the response. Probs are returned whenever type=“prob”. |

`both` |
returns both the vector of fitted responses and class probability estimates. The first element returns the fitted responses and will be labeled as ‘class’. The second element returns the class probability estimates and will be labeled as ‘prob’. |

`F` |
this can be used in the multiclass case. |

This function is invoked by the `summary`

S3 generics invoked with an `maboost`

object. If an error occurs in one of the above
commands then try using this command directly to track possible errors.
Also, the newdata data set must be of type ‘data.frame’ when invoking
`summary`

.

`summary.maboost`

,`print.maboost`

,
`update.maboost`

maboost documentation built on May 19, 2017, 10:52 p.m.

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