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naive.bayes {bnlearn}  R Documentation 
Naive Bayes classifiers
Description
Create, fit and perform predictions with naive Bayes and TreeAugmented 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 = NULL, blacklist = NULL, 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 normalized if
not already so. The default prior is the probability distribution of the training variable in

whitelist 
a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs to be included in the graph. 
blacklist 
a data frame with two columns (optionally labeled "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 ChowLiu
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 starshaped 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
.
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, i.e. 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. Morevoer it is
not possible to whitelist or blacklist arcs incident on training
.
predict()
accepts either a bn
or a bn.fit
object as its first
argument. For the former, the parameters of the network are fitted on data
, that is, the
observations whose class labels the function is trying to predict.
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) # this is an insample prediction with naive Bayes (parameter learning # is performed implicitly during the prediction). bn = naive.bayes(learning.test, "A") pred = predict(bn, learning.test) table(pred, learning.test[, "A"]) # this is an insample prediction with TAN (parameter learning is # performed explicitly with bn.fit). 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 outofsample 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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