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/chr...