Master Optuna: Advanced Hyperparameter Tuning for Machine Learning (Step-by-Step)

Welcome to the ultimate guide on mastering Optuna for advanced hyperparameter tuning in machine learning! In this step-by-step tutorial, I’ll show you how to optimize your models efficiently using Optuna’s powerful search algorithms. Whether you're a seasoned data scientist or just starting out, you'll learn practical strategies to enhance your machine learning workflows with real-world examples. Discover why Optuna is the go-to tool for data scientists aiming for peak model performance. Thanks for watching my video. I share new Data Science videos weekly, you can subscribe for more videos like this.
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[@onurdatascience]

Thanks for watching! I hope you enjoyed the video. For more Data content you can subscribe to my channel, I share new videos every week.
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[@kevon217]

Great demo and thanks for sharing. Trying to learn more about these hyperparameter tuning approaches.
Do you have any suggestions for using optuna or other such approaches for optimizing sampling procedures in large imbalanced datasets with lots of diverse features/data types? I’m working with a multi-faceted LLM dataset and I’m trying to better understand the various distributions and it seems I need a more sound hierarchical stratification approach to sampling records, but I’m a little overwhelmed with fleshing that out. I’m basically trying to ensure that my train-val-test splits are balanced and reflect these nuances in ways a simple random sample will miss.

[@onurdatascience]

Thanks! Using Optuna to optimize a hierarchical stratification process is a great idea-it'll let you fine-tune your sampling strategy across different features. You might also look into more advanced stratification methods that go beyond random sampling to account for all those feature distributions. SMOTE, though usually for balancing classes, could potentially help augment underrepresented groups if that's part of the challenge too. Thanks for watching!

[@hikmetcatak3967]

Thanks. I'll have to use in competition.

[@onurdatascience]

Thanks for watching!