I just tried to use naiveBayes function in e1071 package. Here is the process:
>library(e1071)
>data(iris)
>head(iris, n=5)
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
>model <-naiveBayes(Species~., data = iris)
> pred <- predict(model, newdata = iris, type = 'raw')
> head(pred, n=5)
setosa versicolor virginica
[1,] 1.00000 2.981309e-18 2.152373e-25
[2,] 1.00000 3.169312e-17 6.938030e-25
[3,] 1.00000 2.367113e-18 7.240956e-26
[4,] 1.00000 3.069606e-17 8.690636e-25
[5,] 1.00000 1.017337e-18 8.885794e-26
So far, everything is fine. In the next step, I tried to create a new data point and used the naivebayes model (model) to predict the class variable (Species) and I chose one of the training data points.
> test = c(5.1, 3.5, 1.4, 0.2)
> prob <- predict(model, newdata = test, type=('raw'))
and here is the result:
> prob
setosa versicolor virginica
[1,] 0.3333333 0.3333333 0.3333333
[2,] 0.3333333 0.3333333 0.3333333
[3,] 0.3333333 0.3333333 0.3333333
[4,] 0.3333333 0.3333333 0.3333333
and is strange. The data point I used as test is the row of iris dataset. Based on the actual data, the class variable of this data point is setosa:
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
and the naiveBayes predicted correctly:
setosa versicolor virginica
[1,] 1.00000 2.981309e-18 2.152373e-25
but when I try to predict test data point, it returns incorrect results. Why it returns four rows as predicted when I'm looking for prediction of just one data point? Am I doing wrong?
You need column names that correspond to your training data column names. Your training data
test2 = iris[1,1:4]
predict(model, newdata = test2, type=('raw'))
setosa versicolor virginica
[1,] 1 2.981309e-18 2.152373e-25
"New" test data defined with data.frame
test1 = data.frame(Sepal.Length = 5.1, Sepal.Width = 3.5, Petal.Length = 1.4, Petal.Width = 0.2)
predict(model, newdata = test1, type=('raw'))
setosa versicolor virginica
[1,] 1 2.981309e-18 2.152373e-25
If you only feed it one dimension, then it can predict via the Bayes rule.
predict(model, newdata = data.frame(Sepal.Width = 3), type=('raw'))
setosa versicolor virginica
[1,] 0.2014921 0.3519619 0.446546
If you feed it a dimension not found in the training data, you get equally likely classes. Inputting a longer vector just gives you more predictions.
predict(model, newdata = 1, type=('raw'))
setosa versicolor virginica
[1,] 0.3333333 0.3333333 0.3333333