Prepared by VOLKAN OBAN

Naive Bayes in R>library("caret")>data(iris)

> x = iris[,-5]> y = iris$Species> library("caret")> model = train(x,y,'nb',trControl=trainControl(method='cv',number=10))> modelNaive Bayes

150 samples 4 predictor 3 classes: 'setosa', 'versicolor', 'virginica'

No pre-processingResampling: Cross-Validated (10 fold) Summary of sample sizes: 135, 135, 135, 135, 135, 135, ... Resampling results across tuning parameters:

usekernel Accuracy Kappa FALSE 0.9466667 0.92 TRUE 0.9600000 0.94

Tuning parameter 'fL' was held constant at a value of 0

Tuning parameter 'adjust' was held constant at a value of 1Accuracy was used to select the optimal model using the largest value.The final values used for the model were fL = 0, usekernel = TRUE and adjust = 1. > predict(model$finalModel,x)$class [1] setosa setosa setosa setosa setosa [6] setosa setosa setosa setosa setosa [11] setosa setosa setosa setosa setosa [16] setosa setosa setosa setosa setosa [21] setosa setosa setosa setosa setosa [26] setosa setosa setosa setosa setosa [31] setosa setosa setosa setosa setosa [36] setosa setosa setosa setosa setosa [41] setosa setosa setosa setosa setosa [46] setosa setosa setosa setosa setosa [51] versicolor versicolor versicolor versicolor versicolor [56] versicolor versicolor versicolor versicolor versicolor [61] versicolor versicolor versicolor versicolor versicolor [66] versicolor versicolor versicolor versicolor versicolor [71] virginica versicolor versicolor versicolor versicolor [76] versicolor versicolor virginica versicolor versicolor [81] versicolor versicolor versicolor virginica versicolor [86] versicolor versicolor versicolor versicolor versicolor

[91] versicolor versicolor versicolor versicolor versicolor [96] versicolor versicolor versicolor versicolor versicolor[101] virginica virginica virginica virginica virginica [106] virginica versicolor virginica virginica virginica [111] virginica virginica virginica virginica virginica [116] virginica virginica virginica virginica versicolor[121] virginica virginica virginica virginica virginica [126] virginica virginica virginica virginica virginica [131] virginica virginica virginica versicolor virginica [136] virginica virginica virginica virginica virginica [141] virginica virginica virginica virginica virginica [146] virginica virginica virginica virginica virginica Levels: setosa versicolor virginica

$posterior setosa versicolor virginica [1,] 1.000000e+00 3.122328e-09 8.989129e-11 [2,] 9.999999e-01 4.953302e-08 1.361560e-09 [3,] 1.000000e+00 1.949717e-08 1.152761e-09 [4,] 1.000000e+00 1.146273e-08 6.616756e-10 [5,] 1.000000e+00 8.839954e-10 8.567477e-11 [6,] 1.000000e+00 3.818715e-09 5.965843e-09 [7,] 1.000000e+00 7.394006e-09 6.702907e-10 [8,] 1.000000e+00 5.311568e-09 1.920277e-10 [9,] 1.000000e+00 6.502476e-09 3.193962e-10 [10,] 9.999998e-01 1.731985e-07 5.531788e-09 [11,] 1.000000e+00 1.233528e-09 4.372981e-10 [12,] 1.000000e+00 6.936685e-09 4.552987e-10 [13,] 9.999998e-01 2.398420e-07 8.627082e-09..........

> table(predict(model$finalModel,x)$class,y) y setosa versicolor virginica setosa 50 0 0 versicolor 0 47 3 virginica 0 3 47

> naive_iris <- NaiveBayes(iris$Species ~ ., data = iris)

> plot(naive_iris)

Ref: http://rischanlab.github.io/NaiveBayes.html

Rischan Mafrur, http://rischanlab.github.io