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 notifications@github.com 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

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[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.