Machine Learning
Support Vector Machine
A classification algorithm that finds the optimal hyperplane (decision boundary) that maximizes the margin between different classes. SVMs are effective in high-dimensional spaces.
Why It Matters
SVMs were the go-to algorithm before deep learning and remain effective for text classification, bioinformatics, and small-to-medium datasets where interpretability matters.
Example
An SVM finding the widest possible gap between spam and non-spam emails in feature space, with the decision boundary in the middle of that gap.
Think of it like...
Like drawing a line to separate two groups of dots on paper, but choosing the line that maximizes the gap between the two groups — giving the most confident separation.