Timeline for Classifier or regression for binary system?
Current License: CC BY-SA 3.0
8 events
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| Dec 5, 2013 at 17:23 | comment | added | JEquihua | I really have no idea. Maybe it is related to this question I answered a while ago: stats.stackexchange.com/questions/77904/… | |
| Dec 5, 2013 at 16:59 | comment | added | user35581 | Yeah, I agree. I am thinking that by sampling a small set maybe individual trees get really good at picking out a particular set of traits that is predictive. Maybe there are 10 different profiles of a user who clicks on an ad, and by creating small subsets, each of those profiles gets trained on (randomly) whereas in a larger set of 100's of samples, those profiles would all kind of average out together and become more meaningless... I'm not sure if my theory has any validity, I'm just trying to brainstorm why such a small set might actually be useful... | |
| Dec 5, 2013 at 16:37 | comment | added | JEquihua | the default takes a sample of size 0.632 of your whole data set (rounded up) for each tree of the ensemble, since it is a bootstrap sample you would end up with a proportion similar to the whole: 0.1 1's and 0.9 0's. It's a tad strange that you get better results with (10,10). It's a tiny sample! Usually a little bit more is better. I guess the numbers speak for themselves though. | |
| Dec 5, 2013 at 16:26 | comment | added | user35581 | Thanks - this helps a lot. Is the default randomforest sample size the entire data set? When I compare my model against my test data set, 60 + 60 seems to be a good start, and when I use 10 + 10 it produces even better results. I don't have a good grasp on how I choose the sample sizes, although the trial and error approach seems to kind of work. | |
| Dec 5, 2013 at 16:14 | history | edited | JEquihua | CC BY-SA 3.0 |
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| Dec 5, 2013 at 16:00 | vote | accept | user35581 | ||
| Dec 4, 2013 at 23:25 | history | edited | JEquihua | CC BY-SA 3.0 |
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| Dec 4, 2013 at 23:18 | history | answered | JEquihua | CC BY-SA 3.0 |