Hi CD, No worries.
On Tue, Aug 11, 2015 at 5:11 PM, CD Athuraliya <chathur...@wso2.com> wrote: > Hi Nirmal, > > We will be able to fix this issue. > > Thanks Thushan for pointing this out! :) > > > > On Tue, Aug 11, 2015 at 12:32 PM, Nirmal Fernando <nir...@wso2.com> wrote: > >> @CD, is this something we could fix? can we list features in the order of >> the indices? >> >> On Tue, Aug 11, 2015 at 12:25 PM, Thushan Ganegedara <thu...@gmail.com> >> wrote: >> >>> Hi, >>> >>> I noticed that, in certain cases, the features don't follow the correct >>> ordering. Any idea why this is happening? >>> >>> For example in this image, V10 appears after V1 >>> >>> On Tue, Aug 11, 2015 at 12:10 PM, Thushan Ganegedara <thu...@gmail.com> >>> wrote: >>> >>>> Hi all, >>>> >>>> After a daunting struggle, I was able to corner the issue with the poor >>>> accuracy for the specific leaf dataset. The dataset has classes from 1 to >>>> 36. However, there are no classes from 16th - 22nd. i.e. Classes go as >>>> 1,2,..,14,15,23,24,...,35,36 >>>> >>>> Then, while converting these class labels to enums in H-2-O (combined >>>> with the fact that there's very little data for each class) confuses H-2-O >>>> and causes it to *assign different enum values for the same classes in >>>> different datasets*. Which manifest itself as a poor accuracy. >>>> >>>> I suspect that there's a mismatch between the labels provided by >>>> JavaRDD and enums produced by H-2-O as well. I'm looking into this issue >>>> right now. >>>> >>>> Thank you >>>> >>>> On Mon, Aug 10, 2015 at 11:16 AM, Thushan Ganegedara <thu...@gmail.com> >>>> wrote: >>>> >>>>> Hi all, >>>>> >>>>> I've been testing the new Deeplearning component with few different >>>>> datasets (mainly leaf dataset) and the leaf dataset seems to be not >>>>> working >>>>> as expected for an unknown reason. >>>>> >>>>> However, I tested the Deeplearning component extensively with the leaf >>>>> dataset and identified several potential problems that might be causing >>>>> the >>>>> poor accuracy. >>>>> >>>>> 1. Need to have higher number of epochs (compared to other datasets) >>>>> to produce a reasonable accuracy. >>>>> >>>>> 2. Too many neurons causing overfitting thereby causing poor accuracy. >>>>> >>>>> 3. Some classes have quite closely related features (Especially the >>>>> latter classes are misclassified often) >>>>> >>>>> I was able to get an accuracy of 86% with Logistic Regression L-BFGS. >>>>> Which is quite reasonable. But I'm having trouble reaching that accuracy >>>>> with Deeplearning (which should be quite easy). Highest accuracy I reached >>>>> so far is 71.xx% >>>>> >>>>> So I'm still looking for any definite issues causing the poor accuracy. >>>>> >>>>> Thank you. >>>>> >>>>> >>>>> -- >>>>> Regards, >>>>> >>>>> Thushan Ganegedara >>>>> School of IT >>>>> University of Sydney, Australia >>>>> >>>> >>>> >>>> >>>> -- >>>> Regards, >>>> >>>> Thushan Ganegedara >>>> School of IT >>>> University of Sydney, Australia >>>> >>> >>> >>> >>> -- >>> Regards, >>> >>> Thushan Ganegedara >>> School of IT >>> University of Sydney, Australia >>> >> >> >> >> -- >> >> Thanks & regards, >> Nirmal >> >> Team Lead - WSO2 Machine Learner >> Associate Technical Lead - Data Technologies Team, WSO2 Inc. >> Mobile: +94715779733 >> Blog: http://nirmalfdo.blogspot.com/ >> >> >> > > > -- > *CD Athuraliya* > Software Engineer > WSO2, Inc. > lean . enterprise . middleware > Mobile: +94 716288847 <94716288847> > LinkedIn <http://lk.linkedin.com/in/cdathuraliya> | Twitter > <https://twitter.com/cdathuraliya> | Blog > <http://cdathuraliya.tumblr.com/> > -- Regards, Thushan Ganegedara School of IT University of Sydney, Australia
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