Package: adabag
Type: Package
Title: Applies multiclass AdaBoost.M1, AdaBoost-SAMME and Bagging
Version: 3.1
Date: 2012-06-04
Author: Alfaro-Cortes, Esteban; Gamez-Martinez, Matias and
Garcia-Rubio, Noelia
Maintainer: Esteban Alfaro-Cortes <Esteban.Alfaro@uclm.es>
Depends: rpart, mlbench, caret
Description: This package implements Freund and Schapire's Adaboost.M1
algorithm and Breiman's Bagging algorithm using classification
trees as individual classifiers. Once these classifiers have
been trained, they can be used to predict on new data. Also,
cross validation predictions can be done.  Since version 2.0
the function "margins" is available to calculate the margins
for these classifiers.  Also a higher flexibility is achieved
giving access to the "rpart.control" argument of "rpart".  Four
important new features were introduced on version 3.0,
AdaBoost-SAMME (Zhu et al., 2009) is implemented and a new
function "errorevol" shows the error of the ensembles as a
function of the number of iterations.  In addition, the
ensembles can be pruned using the option "newmfinal" in the
predict.bagging and predict.boosting functions and the
posterior probability of each class for observations can be
obtained. Version 3.1 modifies the relative importance measure
to take into account the gain of the Gini index given by a
variable in each tree and the weights of these trees.
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2012-07-04 20:22:15 UTC; Esteban.Alfaro
Repository: CRAN
Date/Publication: 2012-07-05 17:18:10

Package: adabag
Type: Package
Title: Applies multiclass AdaBoost.M1, AdaBoost-SAMME and Bagging
Version: 3.2
Date: 2013-08-16
Author: Alfaro, Esteban; Gamez, Matias and Garcia, Noelia
Maintainer: Esteban Alfaro <Esteban.Alfaro@uclm.es>
Depends: rpart, mlbench, caret
Description: This package implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm
using classification trees as individual classifiers. Once these classifiers have been trained,
they can be used to predict on new data. Also, cross validation predictions can be done.
Since version 2.0 the function "margins" is available to calculate the margins for these classifiers.
Also a higher flexibility is achieved giving access to the "rpart.control" argument of "rpart".
Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu et al., 2009) is implemented
and a new function "errorevol" shows the error of the ensembles as a function of the number of iterations.
In addition, the ensembles can be pruned using the option "newmfinal" in the predict.bagging and predict.boosting
functions and the posterior probability of each class for observations can be obtained. Version 3.1 modifies
the relative importance measure to take into account the gain of the Gini index given by a variable in each tree
and the weights of these trees.
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2013-08-16 15:58:44 UTC; Esteban.Alfaro
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-08-16 18:25:06