bnlearn - Bayesian network structure learning

bn.cv {bnlearn}

R Documentation

Cross-validation for Bayesian networks

Description

Perform a k-fold cross-validation for a learning algorithm or a fixed network structure.

Usage

bn.cv(data, bn, loss = NULL, k = 10, algorithm.args = list(), 
  loss.args = list(), fit = "mle", fit.args = list(), debug = FALSE)

Arguments

`data`	a data frame containing the variables in the model.
`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).
`loss`	a character string, the label of a loss function. If none is specified, the default loss function is the Log-Likelihood Loss for both discrete and continuous data sets. See below for additional details.
`k`	a positive integer number, the number of groups into which the data will be split.
`algorithm.args`	a list of extra arguments to be passed to the learning algorithm.
`loss.args`	a list of extra arguments to be passed to the loss function specified by `loss`.
`fit`	a character string, the label of the method used to fit the parameters of the newtork. See `bn.fit` for details.
`fit.args`	additional arguments for the parameter estimation prcoedure, see again `bn.fit` for details..
`debug`	a boolean value. If `TRUE` a lot of debugging output is printed; otherwise the function is completely silent.

Details

The following loss functions are implemented:

Log-Likelihood Loss (logl): also known as negative entropy or negentropy, it's the negated expected log-likelihood of the test set for the Bayesian network fitted from the training set.
Gaussian Log-Likelihood Loss (logl-g): the negated expected log-likelihood for Gaussian Bayesian networks.
Classification Error (pred): the prediction error for a single node (specified by the target parameter in loss.args) in a discrete network.

Value

An object of class bn.kcv.

Author(s)

Marco Scutari

References

Koller D, Friedman N (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.

Examples

bn.cv(learning.test, 'hc', loss = "pred", loss.args = list(target = "F"))
# 
#  k-fold cross-validation for Bayesian networks
#
#  target learning algorithm:             Hill-Climbing 
#  number of subsets:                     10 
#  loss function:                         Classification Error 
#  expected loss:                         0.509 
#
bn.cv(gaussian.test, 'mmhc')
#
#  k-fold cross-validation for Bayesian networks
#
#  target learning algorithm:             Max-Min Hill Climbing 
#  number of subsets:                     10 
#  loss function:                         Log-Likelihood Loss (Gaussian) 
#  expected loss:                         10.63062 
#

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