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View Code? Open in Web Editor NEWOnline Random Forests
Home Page: http://www.ymer.org/amir/software/online-random-forests/
License: MIT License
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dengcy028 a-wiedemann djloco mrgloom mihail812 serendipious zywland biotrump caomw silasxue briancairl mugabi0978 pengjiwu statml hyall myouesfi jagirdarnikhil avidoggy dreamstudio2015 trantorrepository whmnoe4j shubhampachori12110095 shujiehan jennyhsiao azure10h wind-meta salvatore-arienzo shyamalschandra sgoubet-fr onesapling ruiyang-1010 vishalbelsare xuangestallone anjackq szh12031online-random-forests's Issues
Can this explain the features based on time period we use ? Thanks
Data
Understanding parameter
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
Makefile problem
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.
Data format
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
.......
Temporal Weighting Scheme
Is the temporal weighting scheme described in the paper implemented in the code and if so, how is the parameter controlling this adjusted?
sparse matrix input doesn't work
Saving a model for use later
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:
- Train the model on the training data
- Test the model on the test set
- save the current position of the Random Forest
- Load that Random Forest
- Test it again on a different test set
Or is there a way to test it on two test sets stored in two different files and get the metrics for each separately?
I can not find that how to discard tree in your code
Could you tell me where is the code that show how to discard trees when data distribution is changed
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