bn.kcv class {bnlearn} | R Documentation

## The bn.kcv class structure

### Description

The structure of an object of S3 class `bn.kcv` or `bn.kcv.list`.

### Details

An object of class `bn.kcv.list` is a list whose elements are objects of class `bn.kcv`.

An object of class `bn.kcv` is a list whose elements correspond to the iterations of a k-fold cross-validation. Each element contains the following objects:

- `test`: an integer vector, the indexes of the observations used as a test set.

- `fitted`: an object of class `bn.fit`, the Bayesian network fitted from the training set.

- `learning`: the `learning` element of the `bn` object that was used for parameter learning from the training set (either learned from the training set as well or specified by the user).

- `loss`: the value of the loss function.

If the loss function requires to predict values from the test sets, each element also contains:

- `predicted`: a factor or a numeric vector, the predicted values for the target node in the test set.

- `observed`: a factor or a numeric vector, the observed values for the target node in the test set.

In addition, an object of class `bn.kcv` has the following attributes:

- `loss`: a character string, the label of the loss function.

- `mean`: the mean of the values of the loss function computed in the `k` iterations of the cross-validation, which is printed as the "expected loss" or averaged to compute the "average loss over the runs".

- `bn`: either a character string (the label of the learning algorithm to be applied to the training data in each iteration) or an object of class `bn` (a fixed network structure).

### Author(s)

Marco Scutari

[Package bnlearn version 4.7.1 Index]