I want to know..more things on how the algorithms like svm is made parallel
weather MR -ed training phase or prediction or both...
In normal cases training phase is apt for MR as it takes lot of time.
Do we need to MR prediction also?
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*Thanks Regards*
Unmesha Sreeveni U.B
*Junior
Thanks Manuel.
It seems that these two (https://issues.apache.org/jira/browse/MAHOUT-334
and https://issues.apache.org/jira/browse/MAHOUT-232) patches might work,
although not in parallel.
Does anyone has sucessfully used any of these two patches already and could
share some comments about it?
I realized what was the problem.
First of all the data was not big enough to split the job in more than one
task. Training file was 30MB and my block sizes were 64MB.
Besides that, I set the number of map (mapred.map.tasks) and reduce (
mapred.reduce.tasks) tasks in the mapred-site.xml file of
Any specific reasons u r looking for an SVM implementation only?
R u sure that those patches r still relevant given the codebase today?
On Saturday, December 7, 2013 2:58 PM, Fernando Santos
fernandoleandro1...@gmail.com wrote:
Thanks Manuel.
It seems that these two
Hello Suneel,
I want to check if any better performance is reached with SVM.
I've been using naive bayes, but my data is quite unbalanced and therefore
I'm getting pretty bad results with it. I also tried the complementary
naive bayes, but got the same bad results. I read about this difference
Hello Fernando,
The naive bayes approach makes the assumption that your features are
independent, if your featurea have a high correlation, naive bayes won't be
a good choice.
I would advice you to try the neural networks (mlp), it can get a better
decision surface than logistic regression...
