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 <[email protected]> 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
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