I’m new to R and I’m using the e1071 package for SVM classification in R.
I used the following code:
data <- loadNumerical()
model <- svm(data[,-ncol(data)], data[,ncol(data)], gamma=10)
print(predict(model, data[c(1:20),-ncol(data)]))
The loadNumerical is for loading data, and the data are of the form(first 8 columns are input and the last column is classification) :
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
1 39 1 -1 43 -1 1 0 0.9050497 0
2 23 -1 -1 30 -1 -1 0 1.6624974 1
3 50 -1 -1 49 1 1 2 1.5571429 0
4 46 -1 1 19 -1 -1 0 1.3523685 0
5 36 1 1 29 -1 1 1 1.3812029 1
6 27 -1 -1 19 1 1 0 1.9403649 0
7 36 -1 -1 25 -1 1 0 2.3360004 0
8 41 1 1 23 1 -1 1 2.4899738 0
9 21 -1 -1 18 1 -1 2 1.2989637 1
10 39 -1 1 21 -1 -1 1 1.6121595 0
The number of rows in the data is 500.
As shown in the code above, I tested the first 20 rows for prediction. And the output is:
1 2 3 4 5 6 7
0.04906014 0.88230392 0.04910760 0.04910719 0.87302217 0.04898187 0.04909523
8 9 10 11 12 13 14
0.04909199 0.87224979 0.04913189 0.04893709 0.87812890 0.04909588 0.04910999
15 16 17 18 19 20
0.89837037 0.04903778 0.04914173 0.04897789 0.87572114 0.87001066
I can tell intuitively from the result that when the result is close to 0, it means 0 class, and if it’s close to 1 it’s in the 1 class.
But my question is how can I precisely interpret the result: is there a threshold s I can use so that values below s are classified as 0 and values above s are classified as 1 ?
If there exists such s, how can I derive it ?
Since your outcome variable is numeric, it uses the regression formulation of SVM. I think you want the classification formulation. You can change this by either coercing your outcome into a factor, or setting
type="C-classification".Regression:
Classification:
Also, if you want probabilities as your prediction rather than just the raw classification, you can do that by fitting with the probability option.
With Probabilities: