Naive Bayes Example using R
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The document outlines a Naive Bayes model implementation in R using the iris dataset, which includes 150 samples across three species of iris plants. It employs cross-validation for model training, ultimately achieving an accuracy of approximately 96%. The model predicts species classifications of the samples with high precision, yielding a detailed table of predictions versus actual species.
![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](https://image.slidesharecdn.com/naive-170215104248/85/Naive-Bayes-Example-using-R-1-320.jpg)
![[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)](https://image.slidesharecdn.com/naive-170215104248/85/Naive-Bayes-Example-using-R-2-320.jpg)
Naive Bayes Example using R
- 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