https://github.com/halacoded/hyperparameter-tuning

Instead of checking every combination like Grid Search or picking randomly like Random Search, Optuna uses a smart method called Bayesian optimization. This means it learns from previous tries to find better settings faster.Part of CODED Data Science Bootcamp
https://github.com/halacoded/hyperparameter-tuning

classification coded descision-tree hyperparameter-tuning kuwait kuwait-codes machine-learning optun

Last synced: 8 months ago
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Instead of checking every combination like Grid Search or picking randomly like Random Search, Optuna uses a smart method called Bayesian optimization. This means it learns from previous tries to find better settings faster.Part of CODED Data Science Bootcamp

• Host: GitHub
• URL: https://github.com/halacoded/hyperparameter-tuning
• Owner: halacoded
• Created: 2025-06-05T10:37:13.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-06-05T10:40:04.000Z (8 months ago)
• Last Synced: 2025-06-05T11:37:59.045Z (8 months ago)
• Topics: classification, coded, descision-tree, hyperparameter-tuning, kuwait, kuwait-codes, machine-learning, optun
• Language: Jupyter Notebook
• Homepage: https://colab.research.google.com/drive/1GiVP77A5iIJKOpJ01u2cultWzVezcpHY?usp=sharing
• Size: 10.7 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Hyperparameter-Tuning

Instead of checking every combination like Grid Search or picking randomly like Random Search, Optuna uses a smart method called Bayesian optimization. This means it learns from previous tries to find better settings faster.