Comments (3)
PhilippPro
commented on August 21, 2024
I don't know what openml.org is showing but that is not true. The table in R shows following:
mlr.classif.featureless mlr.classif.glmnet mlr.classif.kknn mlr.classif.ranger mlr.classif.rpart
500 100512 91284 65547 92259
mlr.classif.svm mlr.classif.xgboost
70743 72172
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PhilippPro
commented on August 21, 2024
The Azuremachine is giving a big boost, we nearly have 1 000 000 results now. Not bad. Maybe the issue is valid for your runs on the azure machine...
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DanielKuehn87
commented on August 21, 2024
Hm ok, that's weird. Insbesondere bei rpart gibts ja nen ziemlichen Unterschied zwischen unseren Daten und dem openml-Dashboard...
Wenn ich mir die aktuellen Runs anschaue wird auch recht viel für SVM/rpart geladen. Ist also ggf. echt ein Problem mit dem Dashboard.
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Related Issues (20)
- Why is there a number behind flows? HOT 1
- Make model with ranks instead of measure
- Data conversion for xgboost HOT 9
- Do not hardcode cluster functions HOT 1
- Literature HOT 12
- Error runs HOT 5
- Conversion of Hyperparameters HOT 11
- do not use print sprintf HOT 1
- please clean up which functions go into which file
- document all functions at least briefly
- Use OpenML Snapshot database HOT 2
- Find a way to upload errors
- runTime not downloadable for some runs HOT 1
- Run defaults implementieren, falls defaults noch nicht vorhanden
- please link from the OML user account to this repo HOT 2
- Prevent runs of same configurations on same dataset HOT 6
- Regression datasets
- min.node.size in ranger HOT 1
- n does not equal n in resampling HOT 1
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