xvalSpec {MLInterfaces} | R Documentation

## container for information specifying a cross-validated machine learning exercise

### Description

container for information specifying a cross-validated machine learning exercise

### Usage

    xvalSpec(type, niter=0,
             partitionFunc = function(data, classLab,iternum) { (seq_len(nrow(data)))[-iternum] },
             fsFun = function(formula, data) formula )

### Arguments

`type` | a string, "LOO" indicating leave-one-out cross-validation, or "LOG" indicating leave-out-group, or "NOTEST", indicating the entire dataset is used in a single training run.
`niter` | numeric specification of the number of cross-validation iterations to use. Ignored if `type` is `"LOO"`.
`partitionFunc` | function, with parameters data (bound to data.frame), clab (bound to character string), iternum (bound to numeric index into sequence of 1:`niter`). This function's job is to provide the indices of training cases for each cross-validation step. An example is `balKfold.xvspec`, which computes a series of indices that are approximately balanced with respect to frequency of outcome types.
`fsFun` | function, with parameters formula, data. The function must return a formula suitable for defining a model on the basis of the main input data. A candidate fsFun is given in example for fsHistory function.

### Details

If `type == "LOO"`, no other parameters are inspected. If `type == "LOG"` a value for `partitionFunc` must be supplied. We recommend using `balKfold.xvspec(K)`. The values of `niter` and `K` in this usage must be the same. This redundancy will be removed in a future upgrade.

If the `parallel` package is attached and symbol `mc_fork` is loaded, cross-validation will be distributed to cores using `mclapply`.

### Value

An instance of `classifierOutput`, with a special structure. The `RObject` return slot is populated with a list of `niter` cross-validation results. Each element of this list is itself a list with two elements: `test.idx` (the indices of the test set for the associated cross-validation iteration, and `mlans`, the `classifierOutput` generated at each iteration. Thus there are `classifierOutput` instances nested within the main `classifierOutput` returned when a `xvalSpec` is used.

### Author(s)

Vince Carey <stvjc@channing.harvard.edu>

### Examples

    library("MASS")
    data(crabs)
    set.seed(1234)
    #
    # demonstrate cross validation
    #
    nn1cv = MLearn(sp~CW+RW, data=crabs, nnetI, xvalSpec("LOG",
       5, balKfold.xvspec(5)), size=3, decay=.01 )
    nn1cv
    confuMat(nn1cv)
    names(RObject(nn1cv)[[1]])
    RObject(RObject(nn1cv)[[1]]$mlans)

[Package MLInterfaces version 1.70.0 Index]