xgb.cv

From xgboost v0.4-2 by Tong He

Cross Validation

The cross valudation function of xgboost

Usage

xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
  missing = NULL, prediction = FALSE, showsd = TRUE, metrics = list(),
  obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
  verbose = T, print.every.n = 1L, early.stop.round = NULL,
  maximize = NULL, ...)

Arguments

params

the list of parameters. Commonly used ones are:

objectiveobjective function, common ones are
- reg:linearlinear regression
- binary:logisticlogistic regression for classification

data

takes an xgb.DMatrix or Matrix as the input.

nrounds

the max number of iterations

nfold

the original dataset is randomly partitioned into nfold equal size subsamples.

label

option field, when data is Matrix

missing

Missing is only used when input is dense matrix, pick a float value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.

prediction

A logical value indicating whether to return the prediction vector.

showsd

boolean, whether show standard deviation of cross validation

metrics,

list of evaluation metrics to be used in corss validation, when it is not specified, the evaluation metric is chosen according to objective function. Possible options are:

errorbinary classification error rate
rmse


          
            obj
            customized objective function. Returns gradient and second order
gradient with given prediction and dtrain.
          
            feval
            custimized evaluation function. Returns
list(metric='metric-name', value='metric-value') with given
prediction and dtrain.
          
            stratified
            boolean whether sampling of folds should be stratified by the values of labels in data
          
            folds
            list provides a possibility of using a list of pre-defined CV folds (each element must be a vector of fold's indices).
If folds are supplied, the nfold and stratified parameters would be ignored.
          
            verbose
            boolean, print the statistics during the process
          
            print.every.n
            Print every N progress messages when verbose>0. Default is 1 which means all messages are printed.
          
            early.stop.round
            If NULL, the early stopping function is not triggered.
If set to an integer k, training with a validation set will stop if the performance
keeps getting worse consecutively for k rounds.
          
            maximize
            If feval and early.stop.round are set, then maximize must be set as well.
    maximize=TRUE means the larger the evaluation score the better.
          
            ...
            other parameters to pass to params.


    

    
    
      
        
        Details
        The original sample is randomly partitioned into nfold equal size subsamples.
Of the nfold subsamples, a single subsample is retained as the validation data for testing the model, and the remaining nfold - 1 subsamples are used as training data.
The cross-validation process is then repeated nrounds times, with each of the nfold subsamples used exactly once as the validation data.
All observations are used for both training and validation.
Adapted from http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29#k-fold_cross-validation

      
    

    
      
        
        Value
        
If prediction = TRUE, a list with the following elements is returned:
dtadata.tablewith each mean and standard deviation stat for training set and test set
predan array or matrix (for multiclass classification) with predictions for each CV-fold for the model having been trained on the data in all other folds.
If prediction = FALSE, just a data.table with each mean and standard deviation stat for training set and test set is returned.

      
    

    

    
      
      
    

    

    

    
      
        
        Aliases
        
        
          xgb.cv
        
        
      
    

    
      
        
        Examples
          
            data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
history <- xgb.cv(data = dtrain, nround=3, nthread = 2, nfold = 5, metrics=list("rmse","auc"),
                  max.depth =3, eta = 1, objective = "binary:logistic")
print(history)
          
      
      
    
    
       Documentation reproduced from package xgboost, version 0.4-2,
        License: Apache License (== 2.0) | file LICENSE
      
    

    
    

    
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