naive.bayes: Naive Bayes classifiers

In vspinu/bnlearn: Bayesian network structure learning, parameter learning and inference

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 = 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 bn.naive, either fitted or not.
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.
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 Chow-Liu algorithm in TAN. Possible values are mi (discrete mutual information) and mi-g (Gaussian mutual information).
root	a character string, the label of the explanatory variable to bre used as the root of the tree in the TAN classifier.
...	extra arguments from the generic method (currently ignored).
prob	a boolean value. If TRUE the posterior probabilities used for prediction are attached to the predicted values as an attribute called prob.
debug	a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

Details

The naive.bayes functions 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.

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.

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)
bn = naive.bayes(learning.test, "A")
pred = predict(bn, learning.test)
table(pred, learning.test[, "A"])

tan = tree.bayes(learning.test, "A")

fitted = bn.fit(tan, learning.test, method = "bayes")
pred = predict(fitted, learning.test)
table(pred, learning.test[, "A"])

vspinu/bnlearn documentation built on May 3, 2019, 7:08 p.m.