Mastering Hyperparameter Tuning with Optuna: Boost Your Machine Learning Models!

In this comprehensive tutorial, we delve deep into the world of hyperparameter tuning using Optuna, a powerful Python library for optimizing machine learning models. Whether you're a data scientist, machine learning enthusiast, or just looking to improve your model's performance, this video is packed with valuable insights and practical tips to help you harness the full potential of Optuna.

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As a full-time data analyst/scientist at a fintech company specializing in combating fraud within underwriting and risk, I've transitioned from my background in Electrical Engineering to pursue my true passion: data. In this dynamic field, I've discovered a profound interest in leveraging data analytics to address complex challenges in the financial sector.

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[RyanAndMattDataScience]

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[neilansh]

I found about Optuna while working on a Kaggle Competition. This video will help me a lot in Kaggle Competitions. Thanks a lot Ryan 👍💯

[masplacasmaschicas]

I gotta give optuna a try I usually just use a gridsearchcv or randomsearchcv for hyper parameter tuning

[ritamchatterjee]

can't see the right part of the code some terms are not understandable, please provide the github link else make your recording screen larger

btw great videos

[ruihanli]

Great video! Could explain more about the hyperparameter importance here and what kind of insights can you learn from it.

[anatolyalekseev]

Thank you for honest sharing of results! Could it be that train_test_split accidentally created split with unbalanced target? Another reason of getting worse OOS result I can think about is optimizing for mean CV scores without getting variance into account. And the 3rd one I suspect is missing sensitivity study. Like, we found the peak on the train set, but mb in its vicinity it had only valleys or even cliffs (we averaged across data splits but not across neighbor parameters)? And the last option is simple absense of early stopping: the last model can simply be overfit one. Going to recreate your example and find out )

[becavas]

Good Work. Why Optuna is better that gridsearchcv or randomsearchcv?

[introvertwhiz-llip]

sir i don't know why you didn't share the code after making the learning project .Please share sir.
q1) At the end Of the video you didn't get the results. So how is the production code really looks like? Do you do any kind of hyperparameter tuning? Or you go on the basis of your knowledge of the different parameters and the intuitions that you have in order to have a much better hyperparameter tuning?

So could you please share your knowledge with respect to the production level code? Sir.

[deepsuchak.]

Hey man following you since a while.. Big fan!

[thomashadler]

Well presented but the performance is poorer than your baseline, correct?

[trn]

I enjoyed your video, but it looks as if you're not understanding the point of the `random_state` parameter. It allows for reproducibility. If you put in a value 'x' for the `random_state` you'll always get the same output. That's useful for testing.