Learn How to Boost Your Python Sklearn Models with GridsearchCV!

In this video, I will show you how to optimize a support vector machine (SVM) learning model using gridsearchCV from scikit-learn (sklearn). I will also demonstrate this improvement visually using a scatter plot from seaborn.
SVMs are a powerful machine learning algorithm that can be used for both classification and regression tasks. However, they can be difficult to tune, as they have a number of hyperparameters that can affect their performance. GridsearchCV is a powerful tool that can be used to automate the process of tuning hyperparameters.
In this video, I will walk you through the steps involved in using gridsearchCV to optimize an SVM model. I will also show you how to visualize the improvement in performance that is achieved by optimizing the model.
This video is for anyone who wants to learn how to optimize SVM models in Python. No prior knowledge of SVMs or gridsearchCV is required.
Here is the github link to the notebook: github.com/chrisp33/Python-Fo...

[@CJP3]

Have you used gridsearchcv? 👀 🙋🏾‍♂️

[@deltax7159]

Just learned about the optuna library for hyperparameter optimization. Gotta love ol gridsearchCV. Just have to take advantage of parallelization for models like random forest and xgboost. Can take awhile to search without. Great video!

[@CJP3]

@@deltax7159 great build! I’ll also check out optuna

[@benwinstanleymusic]

Thank you so much for this video, really great explanations and presentation with just the right amount of detail.

[@CJP3]

You're very welcome! Have a great day!! 🙏🏽

[@ichrakhammioui8824]

Amazing, thank you so much, your explination is so clear

[@CJP3]

Thank you!!🙏🏽

[@user-dv4fj6kz6f]

Thank you for your great explanation

[@CJP3]

Glad it was helpful!! 🙏🏽

[@pavloOps]

You are the best!! Thank you a lot ❤

[@CJP3]

Thank you very much!!