## File: naiveBayes.Rd

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r-cran-e1071 1.7-3-1
 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107 \name{naiveBayes} \alias{naiveBayes} \alias{naiveBayes.default} \alias{naiveBayes.formula} \alias{print.naiveBayes} \alias{predict.naiveBayes} \title{Naive Bayes Classifier} \description{ Computes the conditional a-posterior probabilities of a categorical class variable given independent predictor variables using the Bayes rule. } \usage{ \S3method{naiveBayes}{formula}(formula, data, laplace = 0, ..., subset, na.action = na.pass) \S3method{naiveBayes}{default}(x, y, laplace = 0, ...) \S3method{predict}{naiveBayes}(object, newdata, type = c("class", "raw"), threshold = 0.001, eps = 0, ...) } \arguments{ \item{x}{A numeric matrix, or a data frame of categorical and/or numeric variables.} \item{y}{Class vector.} \item{formula}{A formula of the form \code{class ~ x1 + x2 + \dots}. Interactions are not allowed.} \item{data}{Either a data frame of predictors (categorical and/or numeric) or a contingency table.} \item{laplace}{positive double controlling Laplace smoothing. The default (0) disables Laplace smoothing.} \item{\dots}{Currently not used.} \item{subset}{For data given in a data frame, an index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.)} \item{na.action}{A function to specify the action to be taken if \code{NA}s are found. The default action is not to count them for the computation of the probability factors. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.)} \item{object}{An object of class \code{"naiveBayes"}.} \item{newdata}{A dataframe with new predictors (with possibly fewer columns than the training data). Note that the column names of \code{newdata} are matched against the training data ones.} \item{type}{If \code{"raw"}, the conditional a-posterior probabilities for each class are returned, and the class with maximal probability else.} \item{threshold}{Value replacing cells with probabilities within \code{eps} range.} \item{eps}{double for specifying an epsilon-range to apply laplace smoothing (to replace zero or close-zero probabilities by \code{theshold}.)} } \value{ An object of class \code{"naiveBayes"} including components: \item{apriori}{Class distribution for the dependent variable.} \item{tables}{A list of tables, one for each predictor variable. For each categorical variable a table giving, for each attribute level, the conditional probabilities given the target class. For each numeric variable, a table giving, for each target class, mean and standard deviation of the (sub-)variable.} } \details{ The standard naive Bayes classifier (at least this implementation) assumes independence of the predictor variables, and Gaussian distribution (given the target class) of metric predictors. For attributes with missing values, the corresponding table entries are omitted for prediction. } \author{David Meyer \email{David.Meyer@R-project.org}. Laplace smoothing enhancement by Jinghao Xue.} \examples{ ## Categorical data only: data(HouseVotes84, package = "mlbench") model <- naiveBayes(Class ~ ., data = HouseVotes84) predict(model, HouseVotes84[1:10,]) predict(model, HouseVotes84[1:10,], type = "raw") pred <- predict(model, HouseVotes84) table(pred, HouseVotes84$Class) ## using laplace smoothing: model <- naiveBayes(Class ~ ., data = HouseVotes84, laplace = 3) pred <- predict(model, HouseVotes84[,-1]) table(pred, HouseVotes84$Class) ## Example of using a contingency table: data(Titanic) m <- naiveBayes(Survived ~ ., data = Titanic) m predict(m, as.data.frame(Titanic)) ## Example with metric predictors: data(iris) m <- naiveBayes(Species ~ ., data = iris) ## alternatively: m <- naiveBayes(iris[,-5], iris[,5]) m table(predict(m, iris), iris[,5]) } \keyword{classif} \keyword{category}