Hands-On Hyperparameter Tuning with Scikit-Learn: Tips and Tricks

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  • Опубліковано 6 січ 2025

КОМЕНТАРІ • 13

  • @RyanAndMattDataScience
    @RyanAndMattDataScience  5 місяців тому

    Hey guys I hope you enjoyed the video! If you did please subscribe to the channel!
    Join our Data Science Discord Here: discord.com/invite/F7dxbvHUhg
    If you want to watch a full course on Machine Learning check out Datacamp: datacamp.pxf.io/XYD7Qg
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    *Both Datacamp and Stratascratch are affiliate links.

  • @charlesmay1610
    @charlesmay1610 Місяць тому

    Great tutorial! Helped me a lot in understanding how to implement this in my current project!

  • @vishnukp6470
    @vishnukp6470 Рік тому +2

    i was searching for hyperparameters in your channel

  • @henry-o8i
    @henry-o8i 9 місяців тому +1

    Thank you so much for your content. Always concise and on point, and also able to repeat by following. Saved a lot of times from reading stack overflow while not knowing what I am looking for. Also, I can do hyperparameter tuning on a pipeline as well? instead of classifier, i can just put the pipeline there?

    • @RyanAndMattDataScience
      @RyanAndMattDataScience  9 місяців тому +2

      Checkout the house and titanic Kaggle projects. I use hyper parameter tuning with pipelines and different models

  • @onurdatascience
    @onurdatascience Рік тому +1

    Great tutorial!!

  • @henry-o8i
    @henry-o8i 9 місяців тому +1

    wonder if you have the code so we can follow? thanks

  • @kapur.betzy2023
    @kapur.betzy2023 Рік тому

    You're consistently delivering outstanding content, and I genuinely appreciate it . I'd like to kindly request your expertise in creating additional advanced end-to-end projects involving SQL, Machine Learning, and Excel. Your contributions have been invaluable, and I eagerly await your forthcoming updates.😃😃

    • @RyanAndMattDataScience
      @RyanAndMattDataScience  Рік тому

      I am working on the Titanic data set project. I know it’s been done a lot but its a great one to look at and will fit in with the ML vids