To allow some misclassification for better generalization
To ensure all data points are classified correctly
To increase the number of support vectors
To reduce the computational complexity
Questions & Step-by-Step Solutions
What is the role of the soft margin in SVM?
Step 1: Understand that SVM stands for Support Vector Machine, which is a type of machine learning model used for classification tasks.
Step 2: Know that a 'margin' in SVM is the space between the decision boundary (the line or hyperplane that separates different classes) and the closest data points from each class.
Step 3: Realize that a 'hard margin' means that no data points can be on the wrong side of the decision boundary, which can be too strict and may not work well with noisy data.
Step 4: Learn that a 'soft margin' allows some data points to be on the wrong side of the boundary, meaning some misclassifications are acceptable.
Step 5: Understand that this flexibility helps the model to better fit the data, especially when there are overlaps between classes or when the data is noisy.
Step 6: Conclude that the soft margin improves the model's ability to generalize, meaning it performs better on new, unseen data.
