Skip to contents

Use cross-validation to find the optimal nrounds

Source: R/mixgb_cv.R
mixgb_cv.Rd

Use cross-validation to find the optimal nrounds for an Mixgb imputer. Note that this method relies on the complete cases of a dataset to obtain the optimal nrounds.

Usage

mixgb_cv(
  data,
  nfold = 5,
  nrounds = 100,
  early_stopping_rounds = 10,
  response = NULL,
  select_features = NULL,
  xgb.params = list(),
  stringsAsFactors = FALSE,
  verbose = TRUE,
  ...
)

Arguments

data

A data.frame or a data.table with missing values.

nfold

The number of subsamples which are randomly partitioned and of equal size. Default: 5

nrounds

The max number of iterations in XGBoost training. Default: 100

early_stopping_rounds

An integer value k. Training will stop if the validation performance has not improved for k rounds.

response

The name or the column index of a response variable. Default: NULL (Randomly select an incomplete variable).

select_features

The names or the indices of selected features. Default: NULL (Select all the other variables in the dataset).

xgb.params

A list of XGBoost parameters. For more details, please check XGBoost documentation on parameters.

stringsAsFactors

A logical value indicating whether all character vectors in the dataset should be converted to factors.

verbose

A logical value. Whether to print out cross-validation results during the process.

...

Extra arguments to be passed to XGBoost.

Value

A list of the optimal nrounds, evaluation.log and the chosen response.

Examples

params <- list(max_depth = 3, subsample = 0.7, nthread = 2)
cv.results <- mixgb_cv(data = nhanes3, xgb.params = params)
#> Multiple eval metrics are present. Will use test_rmse for early stopping.
#> Will train until test_rmse hasn't improved in 10 rounds.
#> 
#> [1]	train-rmse:1.325541±0.054794	test-rmse:1.328004±0.200554 
#> [2]	train-rmse:1.120618±0.057377	test-rmse:1.120987±0.228403 
#> [3]	train-rmse:0.977183±0.060819	test-rmse:1.008580±0.243726 
#> [4]	train-rmse:0.890541±0.060028	test-rmse:0.943404±0.259013 
#> [5]	train-rmse:0.838193±0.062854	test-rmse:0.900796±0.266055 
#> [6]	train-rmse:0.790665±0.062452	test-rmse:0.871239±0.274423 
#> [7]	train-rmse:0.759332±0.064733	test-rmse:0.857790±0.279167 
#> [8]	train-rmse:0.741639±0.063543	test-rmse:0.849731±0.279353 
#> [9]	train-rmse:0.730104±0.063602	test-rmse:0.850516±0.277500 
#> [10]	train-rmse:0.702745±0.048944	test-rmse:0.858593±0.274182 
#> [11]	train-rmse:0.691603±0.050012	test-rmse:0.860244±0.273899 
#> [12]	train-rmse:0.687875±0.048874	test-rmse:0.859973±0.274179 
#> [13]	train-rmse:0.681551±0.050469	test-rmse:0.863008±0.274802 
#> [14]	train-rmse:0.664586±0.041199	test-rmse:0.863226±0.276052 
#> [15]	train-rmse:0.660445±0.041594	test-rmse:0.862346±0.276682 
#> [16]	train-rmse:0.656897±0.043685	test-rmse:0.862776±0.276146 
#> [17]	train-rmse:0.649109±0.040902	test-rmse:0.861455±0.272649 
#> Stopping. Best iteration:
#> [18]	train-rmse:0.638577±0.033485	test-rmse:0.873910±0.267153
#> 
#> [18]	train-rmse:0.638577±0.033485	test-rmse:0.873910±0.267153 
cv.results$best.nrounds
#> [1] 8

imputed.data <- mixgb(data = nhanes3, m = 3, xgb.params = params,
                      nrounds = cv.results$best.nrounds)