Also you can see in the final iteration of the model that there are 8 true positives and 8 false positives. So this model classifies everything as positive. At that you know that it's not a good model.
Joel Bernstein http://joelsolr.blogspot.com/ On Thu, Feb 9, 2017 at 11:03 AM, Joel Bernstein <joels...@gmail.com> wrote: > A few things that I see right off: > > 1) 2500 terms is too many. I was testing with 100-250 terms > 2) 1000 iterations is to high. If the model hasn't converged by 100 > iterations it's likely not going to converge. > 3) You're going to need more examples. You may want to run features first > and see what it selects. Then you need multiple examples for each feature. > I was testing with the enron ham/spam data set. It would be good to > download that dataset and see what that looks like. > > Joel Bernstein > http://joelsolr.blogspot.com/ > > On Thu, Feb 9, 2017 at 10:15 AM, Susheel Kumar <susheel2...@gmail.com> > wrote: > >> Hello Joel, >> >> Here is the final iteration in json format. >> >> https://www.dropbox.com/s/g3a3606ms6cu8q4/final_iteration.json?dl=0 >> >> Below is the expression used >> >> update(models, >> batchSize="50", >> train(trainingSet, >> features(trainingSet, >> q="*:*", >> featureSet="threatFeatures", >> field="body_txt", >> outcome="out_i", >> numTerms=2500), >> q="*:*", >> name="threatModel", >> field="body_txt", >> outcome="out_i", >> maxIterations="1000")) >> >> I just have 16 documents with 8+ve and 8-ves. The field which contains the >> feedback is body_txt (text_general type) >> >> Thanks for looking. >> >> >> >> On Wed, Feb 8, 2017 at 7:52 AM, Joel Bernstein <joels...@gmail.com> >> wrote: >> >> > Can you post the final iteration of the model? >> > >> > Also the expression you used to train the model? >> > >> > How much training data do you have? Ho many positive examples and >> negatives >> > examples? >> > >> > Joel Bernstein >> > http://joelsolr.blogspot.com/ >> > >> > On Tue, Feb 7, 2017 at 2:14 PM, Susheel Kumar <susheel2...@gmail.com> >> > wrote: >> > >> > > Hello, >> > > >> > > I am tried to follow http://joelsolr.blogspot.com/ to see if we can >> > > classify positive & negative feedbacks using streaming expressions. >> All >> > > works but end result where probability_d result of classify expression >> > > gives similar results for positive / negative feedback. See below >> > > >> > > What I may be missing here. Do i need to put more data in training >> set >> > or >> > > something else? >> > > >> > > >> > > { "result-set": { "docs": [ { "body_txt": [ "love the company" ], >> > > "score_d": 2.1892474120319667, "id": "6", "probability_d": >> > > 0.977944433135261 }, { "body_txt": [ "bad experience " ], "score_d": >> > > 3.1689453250842914, "id": "5", "probability_d": 0.9888109278133054 }, >> { >> > > "body_txt": [ "This company rewards its employees, but you should only >> > work >> > > here if you truly love sales. The stress of the job can get to you and >> > they >> > > definitely push you." ], "score_d": 4.621702323888672, "id": "4", >> > > "probability_d": 0.9999999999898557 }, { "body_txt": [ "no chance for >> > > advancement with that company every year I was there it got worse I >> don't >> > > know if all branches of adp but Florence organization was turn over >> rate >> > > would be higher if it was for temp workers" ], "score_d": >> > > 5.288898825826228, "id": "3", "probability_d": 0.9999999999999956 }, { >> > > "body_txt": [ "It was a pleasure to work at the Milpitas campus. The >> team >> > > that works there are professional and dedicated individuals. The >> level of >> > > loyalty and dedication is impressive" ], "score_d": >> 2.5303947056922937, >> > > "id": "2", "probability_d": 0.9999990430778418 }, >> > > >> > >> > >
