Comments (3)
even a C4.5 tree is providing accuracy around 81%. here is the command
rf_ace_win32 -I train.arff -T test.arff -i clas -O yaz.tsv -P predictions.tsv
Original comment by [email protected]
on 15 Jan 2012 at 7:32
from rf-ace.
Thanks for the report. Prediction part is still under development, so it's
likely that for some data it will fail. If you want to grow the predictor (GBT)
with all the features in the train data, then -- in this version -- you have to
provide a "noFilter" flag as follows:
rf_ace_win32 -I train.arff --noFilter -T test.arff -i clas -O yaz.tsv -P
predictions.tsv
meaning that the Random Forest algorithm won't try to remove any of the
features in train.arff prior to growing the GBT.
Thanks for your input!
Original comment by [email protected]
on 15 Jan 2012 at 9:24
from rf-ace.
Original comment by [email protected]
on 15 Feb 2012 at 3:35
- Changed state: Done
from rf-ace.
Related Issues (20)
- Confusing help regarding nodesize parameter HOT 1
- Working with whitelist HOT 2
- Large forests fail to be built HOT 2
- minor: uninitialized warning during make HOT 1
- minor: tar file contains many junk backup files HOT 1
- make test in teh package fails HOT 1
- Hide irrelevant parameters from the print-outs HOT 1
- Move parameter validation logic from options namespace to the main program HOT 1
- Corrected p-values HOT 1
- Improve train prediction caching
- Implement sparse array HOT 1
- Implement new data type: ordinal HOT 1
- terminate called after throwing an instance of 'int' HOT 2
- Compilation error HOT 6
- unknown feature in blacklist causes 'Segmentation fault' HOT 3
- Segmentation fault when using black list feature HOT 2
- Parsing ARFF files HOT 2
- failed to compile on previously successful system
- assert()ion hit during training HOT 2
- Training Gradient boosting trees fail HOT 1
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