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| naive.bayes {bnlearn} | R Documentation |
Naive Bayes classifiers
Description
Create, fit and perform predictions with naive Bayes and Tree-Augmented naive Bayes (TAN) classifiers.
Usage
naive.bayes(x, training, explanatory)
## S3 method for class 'bn.naive'
predict(object, data, prior, ..., prob = FALSE, debug = FALSE)
tree.bayes(x, training, explanatory, whitelist, blacklist,
mi = NULL, root = NULL, debug = FALSE)
## S3 method for class 'bn.tan'
predict(object, data, prior, ..., prob = FALSE, debug = FALSE)
Arguments
training |
a character string, the label of the training variable. |
explanatory |
a vector of character strings, the labels of the explanatory variables. |
object |
an object of class |
x, data |
a data frame containing the variables in the model, which must all be factors. |
prior |
a numeric vector, the prior distribution for the training variable. It is automatically normalised
if not already so. The default prior is the probability distribution of the training variable in
|
whitelist |
a data frame with two columns (optionally labelled "from" and "to"), containing a set of arcs to be included in the graph. |
blacklist |
a data frame with two columns (optionally labelled "from" and "to"), containing a set of arcs not to be included in the graph. |
mi |
a character string, the estimator used for the mutual information coefficients for the Chow-Liu
algorithm in TAN. Possible values are |
root |
a character string, the label of the explanatory variable to be used as the root of the tree in the TAN classifier. |
... |
extra arguments from the generic method (currently ignored). |
prob |
a boolean value. If |
debug |
a boolean value. If |
Details
The naive.bayes() function creates the star-shaped Bayesian network form of a naive Bayes
classifier; the training variable (the one holding the group each observation belongs to) is at the center
of the star, and it has an outgoing arc for each explanatory variable.
If data is specified, explanatory will be ignored, and the labels of the
explanatory variables will be extracted from the data.
predict() performs a supervised classification of the observations by assigning them to the
group with the maximum posterior probability.
Value
naive.bayes() returns an object of class c("bn.naive", "bn"), which behaves
like a normal bn object unless passed to predict(). tree.bayes()
returns an object of class c("bn.tan", "bn"), which again behaves like a normal
bn object unless passed to predict().
predict() returns a factor with the same levels as the training variable from
data. If prob = TRUE, the posterior probabilities used for prediction are
attached to the predicted values as an attribute called prob.
See network classifiers for a complete list of
network classifiers with the respective references.
Note
Since bnlearn does not support networks containing both continuous and discrete
variables, all variables in data must be discrete.
Ties in prediction are broken using Bayesian tie breaking, that is, sampling at random from the tied values. Therefore, setting the random seed is required to get reproducible results.
tan.tree() supports whitelisting and blacklisting arcs but not their directions. Moreover,
it is not possible to whitelist or blacklist arcs incident on training.
Author(s)
Marco Scutari
References
Borgelt C, Kruse R, Steinbrecher M (2009). Graphical Models: Representations for Learning, Reasoning and Data Mining. Wiley, 2nd edition.
Friedman N, Geiger D, Goldszmidt M (1997). "Bayesian Network Classifiers." Machine Learning, 29(2–3):131–163.
Examples
data(learning.test)
tan = tree.bayes(learning.test, "A")
fitted = bn.fit(tan, learning.test, method = "bayes")
pred = predict(fitted, learning.test)
table(pred, learning.test[, "A"])
# this is an out-of-sample prediction, from a training test to a separate
# test set.
training.set = learning.test[1:4000, ]
test.set = learning.test[4001:5000, ]
bn = naive.bayes(training.set, "A")
fitted = bn.fit(bn, training.set)
pred = predict(fitted, test.set)
table(pred, test.set[, "A"])
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