Description Usage Arguments Details Value Note Author(s) References Examples
View source: R/frontendlearning.R
Create, fit and perform predictions with naive Bayes and TreeAugmented naive Bayes (TAN) classifiers.
1 2 3 4 5 6 7 8  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)

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 
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.
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.
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.
Marco Scutari
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  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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