A Support Vector Machine (SVM) is one of the most powerful supervised learning algorithms used in Machine Learning for both classification and regression tasks. It is widely used in fields like image recognition, text categorization, bioinformatics, and face detection due to its high accuracy and ability to handle complex data efficiently.Support Vector Machine is an algorithm that works by finding a decision boundary (hyperplane) that best separates data into different classes. It aims to find the maximum margin — the widest possible distance between the data points of different categories — which ensures better accuracy and generalization.
SVM plots each data item as a point in an n-dimensional space (where n is the number of features).
It then finds the best line or plane that divides the data into distinct groups.
The support vectors are the data points that lie closest to the boundary — they are crucial for defining the position of the decision line.
Once trained, the model can classify new data points accurately based on this separation.
