Hi,
I’m trying to use OnlineLogisticRegression for a two-class classification
problem, but as my classification results are not very good, I wanted to ask
for support to find out if my settings are correct and if I’m using Mahout
correctly. Because if I’m doing it correctly then probably my f
Why is FEATURE_NUMBER != 13?
With 12 features that are already lovely and continuous, just stick them in
elements 1..12 of a 13 long vector and put a constant value at the
beginning of it. Hashed encoding is good for sparse stuff, but confusing
for your case.
Also, it looks like you only pass th
Hi,
Thanks for your comments.
I modified the examples from the mahout in action book, therefore I used the
hashed approach and that's why i used 100 features. I'll adjust the number.
You say that I'm using the same CVE for all features, so you mean i should
create 12 separate CVE for adding
On Thu, Nov 7, 2013 at 9:45 PM, Andreas Bauer wrote:
> Hi,
>
> Thanks for your comments.
>
> I modified the examples from the mahout in action book, therefore I used
> the hashed approach and that's why i used 100 features. I'll adjust the
> number.
>
Makes sense. But the book was doing sparse
Ok, I'll have a look. Thanks! I know mahout is intended for large scale
machine learning, but I guess it shouldn't have problems with such small data
either.
Ted Dunning schrieb:
>On Thu, Nov 7, 2013 at 9:45 PM, Andreas Bauer wrote:
>
>> Hi,
>>
>> Thanks for your comments.
>>
>> I modified
You are correct that it should work with smaller data as well, but the
trade-offs are going to be very different.
In particular, some algorithms are completely infeasible at large scale,
but are very effective at small scale. Some like those used in glmnet
inherently require multiple passes throu
