Create a computer vision system using decision tree algorithms to solve a real-world problem : [Activity] Support Vector

The video tutorial covers the use of support vector classifiers (SVC) in machine learning, focusing on building and training models using scikit-learn. It explains the setup process, model training, and evaluation using cross-validation. The tutorial also delves into hyperparameter tuning for both RBF and linear kernels, using grid search to optimize parameters like gamma and C. The video concludes with a comparison of model performance, highlighting the simplicity and effectiveness of linear models.

What was the best gamma value found during the grid search for the RBF kernel, and what score did it achieve?

Compare the performance of the linear kernel and the RBF kernel based on the results obtained.

What is the final choice of kernel and parameter for the model, and why was it selected?