Practical Data Science using Python - Support Vector Machine Metrics and Polynomial SVM

The video tutorial explains the importance of the loss function in optimizing models, focusing on the hinge loss used in support vector machines (SVM). It details how hinge loss functions for both positive and negative classifications, providing examples. The tutorial also covers practical applications of SVM, including linear and nonlinear classifications, and introduces the kernel trick for handling complex datasets without adding polynomial features.

How does the hinge loss behave when the predicted value is equal to the actual value?

What happens to hinge loss when the predicted value is significantly greater than the boundary?

In the context of SVM, what is the significance of using higher degree polynomial features?

What are the implications of using too many polynomial features in a support vector machine model?