Hi,
On Tue, Apr 12, 2011 at 10:54 AM, Saeed Abu Nimeh wrote:
> I trained a linear svm and did classification. looking at the model I
> have, with a binary response 0/1, the decision values look like this:
> head(svm.model$decision.values)
> 2.5
> 3.1
> -1.0
>
> looking at the fitted values
> head
I trained a linear svm and did classification. looking at the model I
have, with a binary response 0/1, the decision values look like this:
head(svm.model$decision.values)
2.5
3.1
-1.0
looking at the fitted values
head(svm.model$fitted)
1
1
0
So it looks like anything less than or equal 0 is mappe
Hi Yunfei,
On Fri, Apr 8, 2011 at 8:35 PM, Li, Yunfei wrote:
> Hi,
>
> I am studying using SVM functions of e1071 package to do prediction, and I
> found during the training data are "factor" type, then svm.predict() can
> predict data directly by categories; but if response variables are
> "n
Hi,
I am studying using SVM functions of e1071 package to do prediction, and I
found during the training data are "factor" type, then svm.predict() can
predict data directly by categories; but if response variables are "numerical",
the predicted value from svm will be continuous quantitative nu
4 matches
Mail list logo
