bn.cv: Cross-validation for Bayesian networks

In jtrecenti/bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

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(),
  cluster = NULL, 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 Classification Error for Bayesian networks classifiers; otherwise, 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..
cluster	an optional cluster object from package parallel. See parallel integration for details and a simple example.
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 is 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.

• Predictive Correlation (cor): the correlation between the observed and the predicted values for a single node (specified by the target parameter in loss.args) in a Gaussian Bayesian network.

• Mean Squared Error (mse): the mean squared error between the observed and the predicted values for a single node (specified by the target parameter in loss.args) in a Gaussian Bayesian 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.

See Also

bn.boot, rbn, bn.kcv-class.

Examples

bn.cv(learning.test, 'hc', loss = "pred", loss.args = list(target = "F"))

bn.cv(gaussian.test, 'mmhc')

jtrecenti/bnlearn documentation built on May 20, 2019, 3:16 a.m.