r - Predict the class variable using naiveBayes -

i tried use naivebayes function in e1071 package. here 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, fine. in next step, tried create new data point , used naivebayes model (model) predict class variable (species) , chose 1 of training data points.

> test = c(5.1, 3.5, 1.4, 0.2)  > prob <- predict(model, newdata = test, type=('raw')) 

and here 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 strange. data point used test row of iris dataset. based on actual data, class variable of data point setosa:

sepal.length sepal.width petal.length petal.width species 1          5.1         3.5          1.4         0.2  setosa 

and naivebayes predicted correctly:

             setosa   versicolor    virginica    [1,]      1.00000 2.981309e-18 2.152373e-25 

but when try predict test data point, returns incorrect results. why returns 4 rows predicted when i'm looking prediction of 1 data point? doing wrong?

you need column names correspond training data column names. 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 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 feed 1 dimension, can predict via bayes rule.

predict(model, newdata = data.frame(sepal.width = 3), type=('raw'))          setosa versicolor virginica [1,] 0.2014921  0.3519619  0.446546 

if feed dimension not found in training data, equally classes. inputting longer vector gives more predictions.

predict(model, newdata = 1, type=('raw'))          setosa versicolor virginica [1,] 0.3333333  0.3333333 0.3333333