MachineShop (version 3.3.0)

MLControl: Resampling Controls

Description

Structures to define and control sampling methods for estimation of model predictive performance in the MachineShop package.

Usage

BootControl(
  samples = 25,
  weights = TRUE,
  seed = sample(.Machine$integer.max, 1)
)
BootOptimismControl(
  samples = 25,
  weights = TRUE,
  seed = sample(.Machine$integer.max, 1)
)
CVControl(
  folds = 10,
  repeats = 1,
  weights = TRUE,
  seed = sample(.Machine$integer.max, 1)
)
CVOptimismControl(
  folds = 10,
  repeats = 1,
  weights = TRUE,
  seed = sample(.Machine$integer.max, 1)
)
OOBControl(
  samples = 25,
  weights = TRUE,
  seed = sample(.Machine$integer.max, 1)
)
SplitControl(
  prop = 2/3,
  weights = TRUE,
  seed = sample(.Machine$integer.max, 1)
)
TrainControl(weights = TRUE, seed = sample(.Machine$integer.max, 1))

Arguments

samples

number of bootstrap samples.

weights

logical indicating whether to return case weights in resampled output for the calculation of performance metrics.

seed

integer to set the seed at the start of resampling.

folds

number of cross-validation folds (K).

repeats

number of repeats of the K-fold partitioning.

prop

proportion of cases to include in the training set (0 < prop < 1).

Value

Object that inherits from the MLControl class.

Details

BootControl constructs an MLControl object for simple bootstrap resampling in which models are fit with bootstrap resampled training sets and used to predict the full data set (Efron and Tibshirani 1993).

BootOptimismControl constructs an MLControl object for optimism-corrected bootstrap resampling (Efron and Gong 1983, Harrell et al. 1996).

CVControl constructs an MLControl object for repeated K-fold cross-validation (Kohavi 1995). In this procedure, the full data set is repeatedly partitioned into K-folds. Within a partitioning, prediction is performed on each of the K folds with models fit on all remaining folds.

CVOptimismControl constructs an MLControl object for optimism-corrected cross-validation resampling (Davison and Hinkley 1997, eq. 6.48).

OOBControl constructs an MLControl object for out-of-bootstrap resampling in which models are fit with bootstrap resampled training sets and used to predict the unsampled cases.

SplitControl constructs an MLControl object for splitting data into a separate training and test set (Hastie et al. 2009).

TrainControl constructs an MLControl object for training and performance evaluation to be performed on the same training set (Efron 1986).

References

Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Chapman & Hall/CRC.

Efron, B., & Gong, G. (1983). A leisurely look at the bootstrap, the jackknife, and cross-validation. The American Statistician, 37(1), 36-48.

Harrell, F. E., Lee, K. L., & Mark, D. B. (1996). Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine, 15(4), 361-387.

Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In IJCAI'95: Proceedings of the 14th International Joint Conference on Artificial Intelligence (vol. 2, pp. 1137-1143). Morgan Kaufmann Publishers Inc.

Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application. Cambridge University Press.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed.). Springer.

Efron, B. (1986). How biased is the apparent error rate of a prediction rule? Journal of the American Statistical Association, 81(394), 461-70.

See Also

set_monitor, set_predict, set_strata, resample, SelectedInput, SelectedModel, TunedInput, TunedModel

Examples

# NOT RUN {
## Bootstrapping with 100 samples
BootControl(samples = 100)

## Optimism-corrected bootstrapping with 100 samples
BootOptimismControl(samples = 100)

## Cross-validation with 5 repeats of 10 folds
CVControl(folds = 10, repeats = 5)

## Optimism-corrected cross-validation with 5 repeats of 10 folds
CVOptimismControl(folds = 10, repeats = 5)

## Out-of-bootstrap validation with 100 samples
OOBControl(samples = 100)

## Split sample validation with 2/3 training and 1/3 testing
SplitControl(prop = 2/3)

## Training set evaluation
TrainControl()

# }