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Home Page: http://www.ymer.org/amir/software/online-random-forests/
License: MIT License
Online Random Forests
Home Page: http://www.ymer.org/amir/software/online-random-forests/
License: MIT License
Hi,
I was wondering if you could detail the significance of the two parameters in the code. The first is "numRandomTests". Is this parameter analogous to mtry in Brieman's random forest (the number of features randomly selected at each node)?
The second is "numProjectionFeatures". In a traditional random forest, the split at each node occurs on one variable. Does this parameter set to >1 create a function that the data is split on? Is there an analogous parameter in traditional random forests?
Thanks,
Daniel
Hi, there
I tried to build this program, and I found the makefile may miss sth:
1)Should
2)-lblas may be needed in LDFLAGS, otherwise it would show ' undefined reference to `daxpy_' '.
thanks.
Hi,
I try to run the online-random-forests with my own data,but it has remained in this state:
root@yuyong-Lenovo-V580c:/usr/local/include/orf# ./Online-Forest -c conf/orf.conf --orf --t2
OnlineMCBoost Classification Package:
Loading config file: conf/orf.conf ... Done.
Loading data file: data/Dai-train.libsvm ...
My data format is as follows,could you tell me what went wrong? Thank you!
9150 94 83 1
28 1:2.0 2:17.0 3:0.415730337079 4:-31.0747663551 5:17.2897196262 6:-0.747191011236 7:1.0 8:1.0 10:12.3831775701 11:2.57009345794 12:12.8504672897 13:49.7663551402 14:2.0 15:1.0 16:23.1308411215 17:41.1214953271 19:5.0 20:4.0 21:3.0 22:2.0 23:27.6285046729 24:1.0 25:2.0 26:3.0 27:4.0 28:5.0 29:9.0 30:9.0 31:9.0 32:9.0 33:3.0 34:-26.8107476636 35:5.0 36:3.0 37:1.0 38:9.0 39:9.0 40:9.0 41:9.0 69:1.0 70:1.0 71:1.0 72:1.0 73:4.0 74:1.0 76:2.0 77:1.0 78:1.0 79:1.0 80:1.0 81:3.0 82:1.0 83:1.0 84:1.0
10 1:1.0 2:11.0 3:1.13520408163 4:25.8928571429 5:-7.8125 6:0.591836734694 7:-0.157303370787 8:1.0 9:1.0 10:17.8571428571 11:16.9642857143 12:43.75 13:9.15178571429 14:2.0 15:3.0 16:21.875 17:25.0558035714 19:-9.43080357143 20:4.0 21:2.0 22:1.0 23:3.0 24:1.0 25:4.35267857143 26:0.502232142857 27:1.0 28:2.0 29:9.0 30:9.0 31:9.0 32:9.0 69:1.0 70:2.0 71:1.0 72:1.0 73:1.0 74:1.0 75:1.0 76:4.0 77:1.0 78:1.0
15 1:1.0 2:13.0 3:2.53805774278 4:-7.40740740741 5:23.0452674897 6:-0.377952755906 7:0.4632885212 8:2.0 10:13.3744855967 11:26.9547325103 12:5.9670781893 13:50.0 14:1.0 15:1.0 16:10.1080246914 17:25.128600823 19:4.0 20:-9.25925925926 21:1.18312757202 22:1.0 23:-7.0987654321 24:-2.31481481481 25:4.16666666667 26:1.0 27:2.0 28:3.0 29:4.0 30:9.0 31:9.0 32:9.0 33:9.0 69:1.0 70:1.0 71:1.0 72:1.0 73:1.0 74:1.0 75:1.0 76:1.0 77:1.0 78:1.0 79:3.0 80:2.0
0 1:2.0 2:14.0 3:1.73695652174 4:1.16822429907 5:42.523364486 6:0.0434782608696 7:0.911138923655 9:1.0 11:3.27102803738 12:24.2990654206 13:47.1962616822 14:2.0 15:1.0 16:36.5654205607 17:23.8609813084 19:1.0 20:2.0 21:3.0 22:5.0 23:9.0 24:9.0 25:9.0 26:9.0 27:8.70327102804 28:1.0 29:3.0 30:4.0 31:1.0 32:2.0 33:4.0 34:5.0 35:9.0 36:9.0 37:9.0 38:9.0 69:2.0 70:1.0 71:1.0 72:2.0 73:3.0 74:1.0 75:1.0 76:1.0 77:1.0 78:1.0 79:1.0 80:1.0 81:2.0
.......
Is the temporal weighting scheme described in the paper implemented in the code and if so, how is the parameter controlling this adjusted?
Hi,
Is there a way to save a model and then test it on another set of data for a later use?
So the steps I'd like to take are:
Or is there a way to test it on two test sets stored in two different files and get the metrics for each separately?
Could you tell me where is the code that show how to discard trees when data distribution is changed
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