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Merge pull request #283 from pommedeterresautee/master

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OTTO Rmarkdown
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Tianqi Chen
2015-05-03 09:09:49 -07:00
parent 5b430ee019 1b95df4e54
commit a8d059902d
26 changed files with 246 additions and 28 deletions
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2
R-package/.Rbuildignore
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@@ -1,3 +1,5 @@
\.o$
\.so$
\.dll$
^.*\.Rproj$
^\.Rproj\.user$

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R-package/DESCRIPTION
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@@ -21,7 +21,7 @@ VignetteBuilder: knitr
Suggests:
knitr,
ggplot2 (>= 1.0.0),
DiagrammeR (>= 0.4),
DiagrammeR (>= 0.6),
Ckmeans.1d.dp (>= 3.3.1),
vcd (>= 1.3)
Depends:

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R-package/NAMESPACE
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# Generated by roxygen2 (4.1.0): do not edit by hand
# Generated by roxygen2 (4.1.1): do not edit by hand
export(getinfo)
export(setinfo)

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R-package/R/xgb.train.R
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@@ -42,8 +42,9 @@
#' \item \code{reg:logistic} logistic regression.
#' \item \code{binary:logistic} logistic regression for binary classification. Output probability.
#' \item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
#' \item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes).
#' \item \code{multi:softprob} same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probability of each data point belonging to each class.
#' \item \code{num_class} set the number of classes. To use only with multiclass objectives.
#' \item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective. Class is represented by a number and should be from 0 to \code{tonum_class}.
#' \item \code{multi:softprob} same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.
#' \item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
#' }
#' \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
@@ -84,7 +85,7 @@
#' \item \code{error} Binary classification error rate. It is calculated as \code{(wrong cases) / (all cases)}. For the predictions, the evaluation will regard the instances with prediction value larger than 0.5 as positive instances, and the others as negative instances.
#' \item \code{merror} Multiclass classification error rate. It is calculated as \code{(wrong cases) / (all cases)}.
#' \item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
#' \item \code{ndcg} Normalized Discounted Cumulative Gain. \url{http://en.wikipedia.org/wiki/NDCG}
#' \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
#' }
#'
#' Full list of parameters is available in the Wiki \url{https://github.com/dmlc/xgboost/wiki/Parameters}.

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R-package/man/agaricus.test.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgboost.R
\docType{data}
\name{agaricus.test}

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R-package/man/agaricus.train.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgboost.R
\docType{data}
\name{agaricus.train}

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R-package/man/getinfo.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/getinfo.xgb.DMatrix.R
\docType{methods}
\name{getinfo}

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R-package/man/nrow-xgb.DMatrix-method.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/nrow.xgb.DMatrix.R
\docType{methods}
\name{nrow,xgb.DMatrix-method}

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R-package/man/predict-xgb.Booster-method.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/predict.xgb.Booster.R
\docType{methods}
\name{predict,xgb.Booster-method}

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R-package/man/predict-xgb.Booster.handle-method.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/predict.xgb.Booster.handle.R
\docType{methods}
\name{predict,xgb.Booster.handle-method}

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R-package/man/setinfo.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/setinfo.xgb.DMatrix.R
\docType{methods}
\name{setinfo}

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R-package/man/slice.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/slice.xgb.DMatrix.R
\docType{methods}
\name{slice}

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R-package/man/xgb.DMatrix.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgb.DMatrix.R
\name{xgb.DMatrix}
\alias{xgb.DMatrix}

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R-package/man/xgb.DMatrix.save.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgb.DMatrix.save.R
\name{xgb.DMatrix.save}
\alias{xgb.DMatrix.save}

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R-package/man/xgb.cv.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgb.cv.R
\name{xgb.cv}
\alias{xgb.cv}

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R-package/man/xgb.dump.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgb.dump.R
\name{xgb.dump}
\alias{xgb.dump}

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R-package/man/xgb.importance.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgb.importance.R
\name{xgb.importance}
\alias{xgb.importance}

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R-package/man/xgb.load.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgb.load.R
\name{xgb.load}
\alias{xgb.load}

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R-package/man/xgb.model.dt.tree.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgb.model.dt.tree.R
\name{xgb.model.dt.tree}
\alias{xgb.model.dt.tree}

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R-package/man/xgb.plot.importance.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgb.plot.importance.R
\name{xgb.plot.importance}
\alias{xgb.plot.importance}

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R-package/man/xgb.plot.tree.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgb.plot.tree.R
\name{xgb.plot.tree}
\alias{xgb.plot.tree}

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R-package/man/xgb.save.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgb.save.R
\name{xgb.save}
\alias{xgb.save}

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R-package/man/xgb.save.raw.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgb.save.raw.R
\name{xgb.save.raw}
\alias{xgb.save.raw}

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R-package/man/xgb.train.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgb.train.R
\name{xgb.train}
\alias{xgb.train}
@@ -48,8 +48,9 @@ xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
\item \code{reg:logistic} logistic regression.
\item \code{binary:logistic} logistic regression for binary classification. Output probability.
\item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
\item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes).
\item \code{multi:softprob} same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probability of each data point belonging to each class.
\item \code{num_class} set the number of classes. To use only with multiclass objectives.
\item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective. Class is a number and should be from 0 \code{tonum_class}
\item \code{multi:softprob} same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.
\item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
}
\item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5

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R-package/man/xgboost.Rd
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% Generated by roxygen2 (4.1.0): do not edit by hand
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/xgboost.R
\name{xgboost}
\alias{xgboost}