PlotNaiveBayes: PlotNaiveBayes

In PDEnaiveBayes: Plausible Naive Bayes Classifier Using PDE

PlotNaiveBayes

Description

Visualize the class-conditional distributions of the Pareto Density estimated naive Bayes model (PDENB).

Usage

PlotNaiveBayes(Model, FeatureNames, ClassNames, DatasetName = "Data",
nrows = 1, FeatureOrder, NumFeaturesPerRow = 4, Colors,
IndividualFigures = FALSE)

Arguments

Model	List with elements Priors,c_2List_Train.
FeatureNames	Character vector of names with a name for each feature contained in the data used to create the naive bayes model.
ClassNames	Character vector of class names to present in the legend of the plots.
DatasetName	Character title for each plot.
nrows	Number of rows inside one plot.
FeatureOrder	Numeric vector representing the order of the features to be displayed.
NumFeaturesPerRow	Maximum number of features to be displayed in one plot.
Colors	Character vector of color names. The length of the vector must be the same as the number of classes within the data modeled by the naive Bayes classifier.
IndividualFigures	Optional boolean: If set to TRUE, it returns a list of the individual figures for customization.

Details

Boundaries are assumed to be zero for plotting.

Value

Cls	[1:n] numerical vector with n numbers defining the classification. It has k unique numbers representing the arbitrary labels of the classification.
Posteriors	[1:n, 1:l] Numeric matrices with posterior probabilities.
DataLikelihoodsPerClass	list of length d, each element is a matrix [1:n,1:k] of interpolated class likelihoods per feature d

Author(s)

Quirin Stier

Examples

Data = as.matrix(iris[,1:4])
Cls = as.numeric(iris[,5])
DatasetName = "Iris"

TrainIdx = c(17, 73, 46, 29, 68, 35, 131, 62, 132, 127, 71, 72, 
144, 99, 93, 13, 38, 21, 102, 53, 36, 111, 114, 96, 57, 74, 145, 
86, 3, 16, 52, 59, 140, 40, 122, 109, 6, 91, 79, 15, 108, 139, 
37, 76, 20, 115, 66, 28, 100, 117, 44, 78, 80, 150, 146, 142, 
9, 90, 45, 58, 134, 11, 87, 125, 141, 118, 136, 48, 124, 47, 
8, 27, 33, 92, 130, 54, 65, 104, 23, 98, 129, 123, 34, 128, 135, 
51, 64, 5, 94, 83, 42, 116, 101, 43, 7, 12, 82, 1, 84, 138, 2, 
56, 4, 106, 120)

TestIdx = c(60, 10, 75, 70, 81, 18, 97, 95, 67, 22, 55, 143, 
88, 24, 105, 26, 119, 31, 107, 63, 41, 61, 32, 147, 89, 14, 121, 
19, 113, 49, 126, 112, 25, 77, 137, 103, 50, 30, 149, 110, 39, 
69, 148, 85, 133)

TrainX = Data[TrainIdx, ]
TestX  = Data[TestIdx, ]
TrainY = Cls[TrainIdx]
TestY  = Cls[TestIdx]

VPDENB = Train_naiveBayes(Data = TrainX, Cls = TrainY, Plausible = FALSE)

FeatureNames = colnames(Data)

PlotNaiveBayes(Model = VPDENB$Model, FeatureNames = FeatureNames)

PDEnaiveBayes documentation built on Nov. 17, 2025, 5:07 p.m.