Hi Ron,
Indeed, it seems that I didn’t read you last answer very well. I’m sorry. It
does work correctly when changing the labels to -1 and +1.
The documentation doesn’t mention this. Therefore, the example in it doesn’t
actually work. I have created a ticket for this:
https://issues.apache.or
Looking at the Java/Scala Doc for this class [1]. Seems like this only
supports +1.0 and -1.0 as labeling and there's no mention you can use any
positive integer.
I tried your use case and using just +1 and -1 actually works fine.
--
Rong
[1]
https://github.com/apache/flink/blob/master/flink-lib
Hi all,
This is just getting stranger… After playing a while, it seems that if I have a
vector that has value of 0 (i.e. all zero’s) it classifies it as -1.0. Any
other value for the vector causes it to classify as 1.0:
=== Predictions
(
Hi Rong,
As you can see in my test data example, I did change the labeling data to 8 and
16 instead of 1 and 0.
If SVM always returns +1.0 or -1.0, that would then indeed explain where the
1.0 is coming from. But, it never gives me -1.0, so there is still something
wrong as it classifies every
Hi Mano,
For the always positive prediction result. I think the standard svmguide
data [1] is labeling data as 0.0 and 1.0 instead of -1.0 and +1.0. Maybe
correcting that should work for your case.
For the change of eval pairs, I think SVM in FlinkML will always return
a +1.0 or -1.0 when you use
Hi guys,
Here I am again. I am playing with Flink ML and was just trying to get the
example to work used in the documentation:
https://ci.apache.org/projects/flink/flink-docs-release-1.5/dev/libs/ml/quickstart.html#loading-data
(the one using the astroparticle LibSVM data).
My code is basicall
