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 is the primary example used to demonstrate SVC in this tutorial?

• Predicting car speed based on distance and size of a bump

• Classifying emails as spam or not spam

• Predicting house prices based on location and size

• Identifying handwritten digits

Which kernel type is initially used with the SVC model in this tutorial?

• Polynomial

• Sigmoid

• RBF

• Linear

What is the purpose of using K-fold cross-validation in this context?

• To simplify the training process

• To evaluate the model's performance

• To reduce the complexity of the model

• To increase the size of the dataset

What is the main hyperparameter associated with the RBF kernel that is tuned in this tutorial?

• Beta

• Alpha

• Delta

• Gamma

What tool is used to experiment with different parameter values for the RBF kernel?

• Grid Search

• Random Search

• Bayesian Optimization

• Genetic Algorithm

Which hyperparameter is tuned for the linear kernel in the tutorial?

• Lambda

• C

• Gamma

• Alpha

What conclusion is drawn about the choice between linear and RBF kernels for this dataset?

• Linear kernel is significantly better

• Both perform similarly, so simpler is preferred

• RBF kernel is significantly better

• Neither kernel is suitable for this dataset