Machine Learning Concept 41: Hard Margin & Soft Margin SVMs.

In a binary classification problem, the hyperplane is a line that divides the data points into two classes. The distance between the hyperplane and the closest data points from each class is known as the margin. In a hard margin SVM, the goal is to find the hyperplane that can perfectly separate the data into two classes without any misclassification. However, this is not always possible when the data is not linearly separable or contains outliers. In such cases, the hard margin SVM will fail to find a hyperplane that can perfectly separate the data, and the optimization problem will have no solution.