Скачать или смотреть Lecture 85: Machine Learning: Support Vector Machine: Grid Search CV with SVM Using Python Part02

In this video, we demonstrate how to perform Grid Search Cross-Validation (CV) with a Support Vector Machine (SVM) using Python. Grid Search is an essential technique for hyperparameter tuning, helping you find the best combination of parameters to improve your SVM model’s performance. We’ll cover how to use scikit-learn to implement Grid Search with SVM, including setting up a grid of hyperparameters, training the model, and evaluating its performance. By the end of the video, you'll be able to apply Grid Search CV effectively to optimize your SVM models.

This is the part 02 of the video. It has two parts. The first part is in Lecture 84.


The link for folder containing all the Support Vector Machine Classifier codes and datasets can be found in the comments

link for next video:    • Lecture 86: Machine Learning: Support Vect...