MLControl | R Documentation |

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

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))

`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
( |

`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).

Object that inherits from the `MLControl`

class.

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.

`set_monitor`

, `set_predict`

,
`set_strata`

,
`resample`

, `SelectedInput`

,
`SelectedModel`

, `TunedInput`

,
`TunedModel`

## 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()

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