Weka is indeed a more complete package of data mining solutions but its aim
is not to support Hadoop whereas it is the aim of Mahout.
The implemented methods are standard data mining methods. If you are
looking for Hadoop support you should ask the Mahout mailing list but if
you have question on Weka itself you should ask the Weka mailing list. Not
all algorithms are easy to migrate to Hadoop and lots of data mining
applications are fine without a Hadoop cluster eg the netflix prize
provided a 'big' public dataset but it was only about 1 GB.
Regards
Bertrand
On Tue, Oct 16, 2012 at 3:46 PM, Rajesh Nikam <rajeshnikam@gmail.com> wrote:
> Hi,
>
> I was looking for logistic regression algorithms on hadoop.
> mahout is one good package to use on hadoop, however I am not able to get
> could results with my experiments.
>
> There are logistic regression algorithms supported with WEKA which I have
> used on Windows.
> I guess I should be able to run these algos from JAR files as is on linux.
>
> java classpath weka.jar weka.classifiers.functions.Logistic R 1.0E8 M
> 6 t lr.arff
>
> Have anyone ported them to take advantage of hadoop ?
>
> How to interpret the output generated from it like what is Coefficients
> and Odds Ratios that could be used for classification ?
>
>
> Options: R 1.0E8 M 6
>
> Logistic Regression with ridge parameter of 1.0E8
> Coefficients...
> Class
> Variable class_1
> ======================
> a1 0
> a2 0
> a3 0
> a4 0.0082
> a5 0.0151
> a6 0.1034
> a7 0
> a8 0
> a9 0
> a10 0.0397
> a11 0.0003
> a13 0.1195
> a14 0.1389
> Intercept 21.487
>
>
> Odds Ratios...
> Class
> Variable class_1
> ======================
> a1 1
> a2 1
> a3 1
> a4 1.0083
> a5 1.0152
> a6 0.9018
> a7 1
> a8 1
> a9 1
> a10 0.961
> a11 0.9997
> a13 0.8873
> a14 0.8703
>
> Time taken to build model: 6.39 seconds
> Time taken to test model on training data: 1.86 seconds
>
> === Error on training data ===
>
> Correctly Classified Instances 49528 99.9173 %
> Incorrectly Classified Instances 41 0.0827 %
> Kappa statistic 0.9983
> Mean absolute error 0.0011
> Root mean squared error 0.0244
> Relative absolute error 0.2202 %
> Root relative squared error 4.895 %
> Total Number of Instances 49569
>
>
> === Confusion Matrix ===
>
> a b < classified as
> 26526 37  a = class_1
> 4 23002  b = class_2
>
>
>
> === Stratified crossvalidation ===
>
> Correctly Classified Instances 49492 99.8447 %
> Incorrectly Classified Instances 77 0.1553 %
> Kappa statistic 0.9969
> Mean absolute error 0.0015
> Root mean squared error 0.0358
> Relative absolute error 0.3108 %
> Root relative squared error 7.1718 %
> Total Number of Instances 49569
>
>
> === Confusion Matrix ===
>
> a b < classified as
> 26532 31  a = class_1
> 46 22960  b = class_2
>
> Thanks in advance.
> Rajesh
>

Bertrand Dechoux
