Naive Bayes Example using R data(iris) ref:http://rischanlab.github.io/NaiveBayes.html Rischan Mafrur

1. Prepared by VOLKAN OBAN
Naive Bayes in R
>library("caret")
>data(iris)
> x = iris[,-5]
> y = iris$Species
> library("caret")
> model = train(x,y,'nb',trControl=trainControl(method='cv',number=10))
> model
Naive Bayes
150 samples
4 predictor
3 classes: 'setosa', 'versicolor', 'virginica'
No pre-processing
Resampling: Cross-Validated (10 fold)
Summary of sample sizes: 135, 135, 135, 135, 135, 135, ...
Resampling results across tuning parameters:
usekernel Accuracy Kappa
FALSE 0.9466667 0.92
TRUE 0.9600000 0.94
Tuning parameter 'fL' was held constant at a value of 0
Tuning parameter 'adjust' was held constant at a value of 1
Accuracy was used to select the optimal model using the
largest value.
The final values used for the model were fL = 0, usekernel =
TRUE and adjust = 1.
> predict(model$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

2. [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)

4. Ref: http://rischanlab.github.io/NaiveBayes.html
Rischan Mafrur, http://rischanlab.github.io