Flash Defect Prediction D2h Results

[amritbhanu]

• Ran only with Decision tree tuning, with an initial population of 12, budget 30, total population size 10000
• Cannot run with the search space what dodge was exploring, the attributes to run with cart/regressor will change depending on which learner or preprocessor to use.
• Results:
  • Flash never wins against dodge but performs as good as dodge in 5 out of 10 datasets.

[timm]

no evidence there that flash is better than dodge. tee hee.

…

On Mon, Apr 8, 2019 at 8:52 PM Amritanshu Agrawal ***@***.***> wrote: - Ran only with Decision tree tuning, with an initial population of 12, budget 30, total population size 10000 - Cannot run with other preprocessors, the attributes to run with cart/regressor will change depending on which learner or preprocessor to use. - Results: - Flash never wins against dodge but performs as good as dodge in 5 out of 10 datasets. [image: flash_d2h] <https://user-images.githubusercontent.com/3458568/55765895-e8ff5d80-5a3f-11e9-8c6b-530c92472dc4.png> — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#21>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AAByC4D_8wLJIK0l1TPeGLng9VCtQS8Nks5ve-RcgaJpZM4cjYPp> .

-- *Tim Menzies * IEEE Fellow, prof, phd, cs, (he/him/his) http://menzies.us skype://menzies.tim +1 304 376 2859

[amritbhanu]

• XGB Tuned using Flash, with a budget of 20.
• Flash never wins, but performs as good as dodge in 5 out of 10 datasets.