makeResampleDesc: Create a description object for a resampling strategy.
In riebetob/mlr: Machine Learning in R

Description Usage Arguments Details Value Standard ResampleDesc objects See Also Examples

A description of a resampling algorithm contains all necessary information to create a ResampleInstance, when given the size of the data set.

1 2	makeResampleDesc(method, predict = "test", ..., stratify = FALSE, stratify.cols = NULL)

`method`	[`character(1)`] “CV” for cross-validation, “LOO” for leave-one-out, “RepCV” for repeated cross-validation, “Bootstrap” for out-of-bag bootstrap, “Subsample” for subsampling, “Holdout” for holdout.
`predict`	[`character(1)`] What to predict during resampling: “train”, “test” or “both” sets. Default is “test”.
`...`	[any] Further parameters for strategies. iters [`integer(1)`] Number of iterations, for “CV”, “Subsample” and “Bootstrap”. split [`numeric(1)`] Proportion of training cases for “Holdout” and “Subsample” between 0 and 1. Default is 2 / 3. reps [`integer(1)`] Repeats for “RepCV”. Here `iters = folds * reps`. Default is 10. folds [`integer(1)]` Folds in the repeated CV for `RepCV`. Here `iters = folds * reps`. Default is 10.
`stratify`	[`logical(1)`] Should stratification be done for the target variable? For classification tasks, this means that the resampling strategy is applied to all classes individually and the resulting index sets are joined to make sure that the proportion of observations in each training set is as in the original data set. Useful for imbalanced class sizes. For survival tasks stratification is done on the events, resulting in training sets with comparable censoring rates.
`stratify.cols`	[`character`] Stratify on specific columns referenced by name. All columns have to be factors. Note that you have to ensure yourself that stratification is possible, i.e. that each strata contains enough observations. This argument and `stratify` are mutually exclusive.

Some notes on some special strategies:

Repeated cross-validation: Use “RepCV”. Then you have to set the aggregation function for your preferred performance measure to “testgroup.mean” via setAggregation.
B632 bootstrap: Use “Bootstrap” for bootstrap and set predict to “both”. Then you have to set the aggregation function for your preferred performance measure to “b632” via setAggregation.
B632+ bootstrap: Use “Bootstrap” for bootstrap and set predict to “both”. Then you have to set the aggregation function for your preferred performance measure to “b632plus” via setAggregation.
Fixed Holdout set: Use makeFixedHoldoutInstance.

Object slots:

id [character(1)]: Name of resampling strategy.
iters [integer(1)]: Number of iterations. Note that this is always the complete number of generated train/test sets, so for a 10-times repeated 5fold cross-validation it would be 50.
predict [character(1)]: See argument.
stratify [logical(1)]: See argument.
All parameters passed in ... under the respective argument name: See arguments.

[ResampleDesc].

For common resampling strategies you can save some typing by using the following description objects:

hout: holdout a.k.a. test sample estimation (two-thirds training set, one-third testing set)
cv2: 2-fold cross-validation
cv3: 3-fold cross-validation
cv5: 5-fold cross-validation
cv10: 10-fold cross-validation

Other resample: ResamplePrediction, ResampleResult, addRRMeasure, getRRPredictionList, getRRPredictions, getRRTaskDescription, getRRTaskDesc, makeResampleInstance, resample

# Bootstraping
makeResampleDesc("Bootstrap", iters = 10)
makeResampleDesc("Bootstrap", iters = 10, predict = "both")

# Subsampling
makeResampleDesc("Subsample", iters = 10, split = 3/4)
makeResampleDesc("Subsample", iters = 10)

# Holdout a.k.a. test sample estimation
makeResampleDesc("Holdout")