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Syllabus | Slides and Assignments | Project | Lecturer

Hyperparameter Tuning

Make sure your repo is up-to-date

Assignment codes might be modified during the semester so please pull from this repo first and overwrite your repo with the Tuning folder.

Make sure your A3 are correct

A10 utilizes my_evaluation in A3. Make sure they are correct before working on A10.

Build your own Genetic Algorithm for tuning model parameters

Implement every function in my_GA.py

Hint: check how A10.py utilizes my_GA.py to tune my_DT learner.

Test my_evaluation Algorithm with A10.py

Example output:

(base) zhe@Zhe-Yus-MacBook-Pro Tuning % python A10.py
[array([0.9553351 , 0.95858508]), array([0.95626124, 0.95786008]), array([0.95663662, 0.95663662])]
[0.95776608]

Results can be quite different due to randomness. You can try executing it multiple times.

Do not forget to push your local changes to the Github server.

Grading Policy

  • importing additional packages such as sklearn is not allowed.
  • 4 (out of 7) points will be received if A10.py successfully runs and finds a parameter set.
  • The rest 3 points will be given based on the correctness of the implementation (TA will examine it manually).

Hint

  • If my_GA.py is too difficult to implement, you can try to complete my_GA_hint.py.
  • Then, remember to rename it as my_GA.py before submitting.