Thanks Yexi...A very nice explanation...Thanks a lot.. Explained in a very simple way which is really understandable for beginners..Thanks a lot. I can go for chaining jobs right?
On Sun, Dec 1, 2013 at 8:55 PM, Yexi Jiang <[email protected]> wrote: > In my opinion. > > 1. Build the decision tree model with the training data. > 2. Store it somewhere. > 3. When the unlabeled data is available: > 3.1 if the unlabeled data is huge, write another mrjob to process them, > load the model at the setup stage, use the model to label the data one by > one in map stage. There is no necessary to have a reducer. > 3.2 if the unlabeled data is small, it is trivial. > > > > > 2013/12/1 unmesha sreeveni <[email protected]> > >> Thanks Yexi , >> >> But how it can be accomplished. >> The input to Desicion Tree MR will be a set of data. But while >> predicting a data it will be a one line data without classlabel right? >> So what changes will be there in mrjob.Should we design like this. >> 1. When a set of data is coming draw Desicion tree >> 2. else if a one line data is coming.check the output of decision >> tree(Decision tree generated from mr) and predict the class label. >> >> ------- >> >> M1_train - dataset for training. >> M1_test - test data or prediction. >> 1. Will it be one data as input for prediction or set of data given >> as input at-once. >> 2.we also need to ensure in our pgm that M1_test belongs to M1_train >> only. we shld check that also ...right? if M1_test is given into >> M2_train it should show error. is nt 'it?. >> >> Pls suggest if my thoughts are wrong. >> >> On 11/30/13, Yexi Jiang <[email protected]> wrote: >> > I watched the video in it but I cannot access its source code due to >> > permission issue. >> > In my opinion, once the decision tree model is built, the model is small >> > enough to be loaded into memory and can be used directly without another >> > mrjob for prediction. The prediction can be conducted in a streaming >> way. >> > >> > >> > 2013/11/30 unmesha sreeveni <[email protected]> >> > >> >> I have gone through a Map Reduce implementation of c4.5 in >> >> >> http://btechfreakz.blogspot.in/2013/04/implementation-of-c45-algorithm-using.html >> >> >> >> Here a decision tree is build. So my doubt is >> >> Can we also include the prediction along with that? >> >> >> >> >> >> On Tue, Nov 26, 2013 at 8:52 AM, Yexi Jiang <[email protected]> >> wrote: >> >> >> >>> You are welcome :) >> >>> >> >>> >> >>> 2013/11/25 unmesha sreeveni <[email protected]> >> >>> >> >>>> ok . Thx Yexi >> >>>> >> >>>> >> >>>> On Tue, Nov 26, 2013 at 1:41 AM, Yexi Jiang <[email protected]> >> >>>> wrote: >> >>>> >> >>>>> As far as I know, there is no ID3 implementation in mahout >> currently, >> >>>>> but you can use the decision forest instead. >> >>>>> https://cwiki.apache.org/confluence/display/MAHOUT/Breiman+Example. >> >>>>> >> >>>>> >> >>>>> 2013/11/25 unmesha sreeveni <[email protected]> >> >>>>> >> >>>>>> Is that ID3 classification? >> >>>>>> It includes prediction also? >> >>>>>> >> >>>>>> >> >>>>>> On Sat, Nov 23, 2013 at 9:01 PM, Yexi Jiang >> >>>>>> <[email protected]>wrote: >> >>>>>> >> >>>>>>> You can directly find it at https://github.com/apache/mahout, or >> you >> >>>>>>> can check out from svn by following >> >>>>>>> >> https://cwiki.apache.org/confluence/display/MAHOUT/Version+Control. >> >>>>>>> >> >>>>>>> >> >>>>>>> 2013/11/23 unmesha sreeveni <[email protected]> >> >>>>>>> >> >>>>>>>> I want to go through Decision tree implementation in mahout. >> >>>>>>>> Refereed Apache Mahout <http://mahout.apache.org/> >> >>>>>>>> >> >>>>>>>> 6 Feb 2012 - Apache Mahout 0.6 released >> >>>>>>>> Apache Mahout has reached version 0.6. All developers are >> encouraged >> >>>>>>>> to begin using version 0.6. Highlights include: >> >>>>>>>> Improved Decision Tree performance and added support for >> regression >> >>>>>>>> problems >> >>>>>>>> >> >>>>>>>> Where can I find its source code and documentation. >> >>>>>>>> >> >>>>>>>> Should I download mahout >> >>>>>>>> >> >>>>>>>> -- >> >>>>>>>> *Thanks & Regards* >> >>>>>>>> >> >>>>>>>> Unmesha Sreeveni U.B >> >>>>>>>> >> >>>>>>>> *Junior Developer* >> >>>>>>>> >> >>>>>>>> >> >>>>>>>> >> >>>>>>> >> >>>>>>> >> >>>>>>> -- >> >>>>>>> ------ >> >>>>>>> Yexi Jiang, >> >>>>>>> ECS 251, [email protected] >> >>>>>>> School of Computer and Information Science, >> >>>>>>> Florida International University >> >>>>>>> Homepage: http://users.cis.fiu.edu/~yjian004/ >> >>>>>>> >> >>>>>>> >> >>>>>> >> >>>>>> >> >>>>>> -- >> >>>>>> *Thanks & Regards* >> >>>>>> >> >>>>>> Unmesha Sreeveni U.B >> >>>>>> >> >>>>>> *Junior Developer* >> >>>>>> >> >>>>>> >> >>>>>> >> >>>>> >> >>>>> >> >>>>> -- >> >>>>> ------ >> >>>>> Yexi Jiang, >> >>>>> ECS 251, [email protected] >> >>>>> School of Computer and Information Science, >> >>>>> Florida International University >> >>>>> Homepage: http://users.cis.fiu.edu/~yjian004/ >> >>>>> >> >>>>> >> >>>> >> >>>> >> >>>> -- >> >>>> *Thanks & Regards* >> >>>> >> >>>> Unmesha Sreeveni U.B >> >>>> >> >>>> *Junior Developer* >> >>>> >> >>>> >> >>>> >> >>> >> >>> >> >>> -- >> >>> ------ >> >>> Yexi Jiang, >> >>> ECS 251, [email protected] >> >>> School of Computer and Information Science, >> >>> Florida International University >> >>> Homepage: http://users.cis.fiu.edu/~yjian004/ >> >>> >> >>> >> >> >> >> >> >> -- >> >> *Thanks & Regards* >> >> >> >> Unmesha Sreeveni U.B >> >> >> >> *Junior Developer* >> >> >> >> >> >> >> > >> > >> > -- >> > ------ >> > Yexi Jiang, >> > ECS 251, [email protected] >> > School of Computer and Information Science, >> > Florida International University >> > Homepage: http://users.cis.fiu.edu/~yjian004/ >> > >> >> >> -- >> *Thanks & Regards* >> >> Unmesha Sreeveni U.B >> >> *Junior Developer* >> > > > > -- > ------ > Yexi Jiang, > ECS 251, [email protected] > School of Computer and Information Science, > Florida International University > Homepage: http://users.cis.fiu.edu/~yjian004/ > > -- *Thanks & Regards* Unmesha Sreeveni U.B *Junior Developer*
