Timeline for Neural network prediction accuracy doesn't match real world results
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| Jul 28, 2017 at 14:19 | comment | added | Mayou36 | Are you sure? 80% is quite large but possible, depending on what you exactly predict (if only win/tie/loose seems realistic). But then there is some significant difference between your 2017 dataset and the rest. Probably mixed up something? Or do you really say that you would have been able to predict the outcomes of the football matches of the last 20 years with 80% accuracy but in 2017 it dropped to 52%? This drop has to come from some corrupt data or similar thing (if you did the test mentioned above right with the yearly splits) | |
| Jul 28, 2017 at 11:53 | comment | added | AndrWeisR | Well, I tried training with data from 1997 to 2007, to predict 2008. Then 1997 to 2008 to predict 2009, 1997-2009 to predict 2010 etc, right up to 1997-2016 to predict 2017. The resultant accuracy was 83%, no different to my original 10-fold cross validation. | |
| Jul 25, 2017 at 7:56 | comment | added | Mayou36 | You're welcome. If you can, I think it is crucial to only predict games after your training data, this can also be the day after (so instead of splitting by years, train on nearly every year instead of the last couple of days of the last year, say.) in order to get a good evaluation of your classifier. | |
| Jul 24, 2017 at 21:34 | comment | added | AndrWeisR | Thank you. It's food for thought, although my data is not explicitly time-dependent. I don't have year, date or round number in my data. There is an implicit time dependence, in that a team's position on the ladder depends on the matches played to that point in the year. But I will try a sliding 10 year training set, evaluated on entire the following year. | |
| Jul 23, 2017 at 8:51 | history | answered | Mayou36 | CC BY-SA 3.0 |