#svmalgorithm

Support Vector Machines (SVMs) are supervised machine learning algorithms employed in classification and regression tasks.

Hyperplane: A hyperplane separates data points of different classes in feature space; it can also be called a decision boundary.

Mathematical Representation: w•x + b = 0

Example:

For instance, if we will classify animals as cat (+1) and dog (-1) by weight and height, then the hyperplane could be

3×Weight + 2×Height - 50 = 0

Support Vectors: These data points are the nearest ones to hyperplane. These points influence the orientation and position of hyperplane.

Margin: The margin is the distance between hyperplane and the nearest data point of either class; SVM tries to maximize margin to increase confidence of classifications.

Hard Margin: Assuming that the data is perfectly separable by hyperplane. Therefore, all points must lie outside of the margin.

Soft Margin: It allows a few misclassifications or margin violations for non-linearly separable data.

Kernel Function: Kernel function transforms the data into higher dimensional space to make it linearly separable.

Types of Kernels:

Linear Kernel: Suitable for linearly separable data.

Polynomial Kernel: For curved boundaries.

Radial Basis Function (RBF): Captures complex relationships.

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