Package e1071 provides with naiveBayes function. It assumes independence of predictors, and assumes Gaussian distribution for metric predictors.
Its example includes iris sample:
> library(e1071) > data(iris) > head(iris) > head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa
Our target variable is Species:
> m <- naiveBayes(Species ~ ., iris)
> m
Naive Bayes Classifier for Discrete Predictors
Call:
naiveBayes.default(x = X, y = Y, laplace = laplace)
A-priori probabilities:
Y
setosa versicolor virginica
0.3333333 0.3333333 0.3333333
Conditional probabilities:
Sepal.Length
Y [,1] [,2]
setosa 5.006 0.3524897
versicolor 5.936 0.5161711
virginica 6.588 0.6358796
Sepal.Width
Y [,1] [,2]
setosa 3.428 0.3790644
versicolor 2.770 0.3137983
virginica 2.974 0.3224966
Petal.Length
Y [,1] [,2]
setosa 1.462 0.1736640
versicolor 4.260 0.4699110
virginica 5.552 0.5518947
Petal.Width
Y [,1] [,2]
setosa 0.246 0.1053856
versicolor 1.326 0.1977527
virginica 2.026 0.2746501
To see its performance (remember that 5th column is Species):
> table(predict=predict(m, iris[, -5]), true=iris[,5])
true
predict setosa versicolor virginica
setosa 50 0 0
versicolor 0 47 3
virginica 0 3 47
For prediction, use predict. When type=”raw” is given, probability is printed:
> predict(m, iris[1:10, -5])
[1] setosa setosa setosa setosa setosa setosa setosa setosa setosa setosa
Levels: setosa versicolor virginica
> predict(m, iris[1:10, -5], type="raw")
setosa versicolor virginica
[1,] 1 2.981309e-18 2.152373e-25
[2,] 1 3.169312e-17 6.938030e-25
[3,] 1 2.367113e-18 7.240956e-26
[4,] 1 3.069606e-17 8.690636e-25
[5,] 1 1.017337e-18 8.885794e-26
[6,] 1 2.717732e-14 4.344285e-21
[7,] 1 2.321639e-17 7.988271e-25
[8,] 1 1.390751e-17 8.166995e-25
[9,] 1 1.990156e-17 3.606469e-25
[10,] 1 7.378931e-18 3.615492e-25