Comments (5)
A:
- I'm working on DE tune parameters of learners (the same learner as canda )right from R package. then compare it with my reproduced results with R as well as theirs, see whether DE can get a better results.
B:
- Tommorrow I will check the second point you mentioned. and then I will fire up that clustering idea.
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Your decision, of course, but just make sure of one thing
if you improvements via tuning are very small, then the whole experiment suffers from "meh" (i.e. who cares). i think that approach A (above) falls into this camp, right?
Now if you've got a rig where the improvement is large (and you do... you used int for the journal) then that is the rig were it is useful to report the relative value of different tuning methods
up to you
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progress?
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Experiments running. R+python generates a lot of issues. I fixed one by one …..
On Mar 4, 2016, at 5:24 PM, Tim Menzies [email protected] wrote:
progress?
—
Reply to this email directly or view it on GitHub #20 (comment).
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if no new errors threw out, I think I can get the results by 22:00 @timm
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Related Issues (20)
- what we concern about Caret?
- with limited evaluation, which is better DE or Caret?
- results of 0209 tuning goal is PRECISION
- results of 0209 tuning goal is F score
- add AUC evaluation
- Try classifier and not regressor
- new data split scheme
- DE vs GridSearch
- auc_randomly_pick
- auc_uniformly_pick
- f1_uniformly_pick
- f1_randomly_pick
- precision_uniformly_pick
- precision_randomly_pick
- Compare DE with actual Caret with their data HOT 4
- check the following codes HOT 3
- DE tune R learner HOT 10
- DE tune R learner Results HOT 1
- reproduce Canadian results---test on tuning data HOT 15
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