xgboost v0.6-2
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by Tong He
Extreme Gradient Boosting
Extreme Gradient Boosting, which is an efficient implementation
of gradient boosting framework. This package is its R interface. The package
includes efficient linear model solver and tree learning algorithms. The package
can automatically do parallel computation on a single machine which could be
more than 10 times faster than existing gradient boosting packages. It supports
various objective functions, including regression, classification and ranking.
The package is made to be extensible, so that users are also allowed to define
their own objectives easily.
Functions in xgboost
| Name | Description | |
| dim.xgb.DMatrix | Dimensions of xgb.DMatrix | |
| cb.print.evaluation | Callback closure for printing the result of evaluation | |
| cb.early.stop | Callback closure to activate the early stopping. | |
| cb.evaluation.log | Callback closure for logging the evaluation history | |
| callbacks | Callback closures for booster training. | |
| agaricus.test | Test part from Mushroom Data Set | |
| cb.reset.parameters | Callback closure for restetting the booster's parameters at each iteration. | |
| print.xgb.cv.synchronous | Print xgb.cv result | |
| print.xgb.DMatrix | Print xgb.DMatrix | |
| predict.xgb.Booster | Predict method for eXtreme Gradient Boosting model | |
| getinfo | Get information of an xgb.DMatrix object | |
| dimnames.xgb.DMatrix | Handling of column names of xgb.DMatrix | |
| slice | Get a new DMatrix containing the specified rows of orginal xgb.DMatrix object | |
| setinfo | Set information of an xgb.DMatrix object | |
| xgb.attr | Accessors for serializable attributes of a model. | |
| print.xgb.Booster | Print xgb.Booster | |
| xgb.create.features | Create new features from a previously learned model | |
| xgb.dump | Save xgboost model to text file | |
| xgb.cv | Cross Validation | |
| xgb.importance | Show importance of features in a model | |
| xgb.DMatrix | Contruct xgb.DMatrix object | |
| xgb.DMatrix.save | Save xgb.DMatrix object to binary file | |
| xgb.model.dt.tree | Parse a boosted tree model text dump | |
| xgb.parameters<- | Accessors for model parameters. | |
| xgb.ggplot.importance | Plot feature importance as a bar graph | |
| xgb.ggplot.deepness | Plot model trees deepness | |
| xgb.load | Load xgboost model from binary file | |
| xgb.save | Save xgboost model to binary file | |
| xgb.save.raw | Save xgboost model to R's raw vector, user can call xgb.load to load the model back from raw vector | |
| xgb.plot.multi.trees | Project all trees on one tree and plot it | |
| xgb.plot.tree | Plot a boosted tree model | |
| xgboost-deprecated | Deprecation notices. | |
| xgb.train | eXtreme Gradient Boosting Training | |
| agaricus.train | Training part from Mushroom Data Set | |
| cb.save.model | Callback closure for saving a model file. | |
| cb.cv.predict | Callback closure for returning cross-validation based predictions. | |
| No Results! | ||
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Details
| Type | Package |
| Date | 2016-11-25 |
| License | Apache License (== 2.0) | file LICENSE |
| URL | https://github.com/dmlc/xgboost |
| BugReports | https://github.com/dmlc/xgboost/issues |
| VignetteBuilder | knitr |
| RoxygenNote | 5.0.1 |
| NeedsCompilation | yes |
| Packaged | 2016-12-17 21:59:49 UTC; hetong007 |
| Repository | CRAN |
| Date/Publication | 2016-12-18 11:23:27 |
| suggests | Ckmeans.1d.dp (>= 3.3.1) , DiagrammeR (>= 0.8.1) , ggplot2 (>= 1.0.1) , igraph (>= 1.0.1) , knitr , rmarkdown , testthat , vcd (>= 1.3) |
| imports | data.table (>= 1.9.6) , magrittr (>= 1.5) , Matrix (>= 1.1-0) , methods , stringi (>= 0.5.2) |
| depends | R (>= 2.15.1) |
| Contributors | Tianqi Chen, Yuan Tang, Tong He, Michael Benesty, Vadim Khotilovich |
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