The classification problem is always interesting to solve, SVM gives great predictions though😁 .
SVM (Support Vector Machine) is a comfortable algorithm to use to solve classification problems and regression too, mostly classification.
The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N — the number of features) that distinctly classifies the data points.
What is Support Vector Machine ?
SVM model basically a representation of two classes using hyperplane in multidimensional space (N-dimentional space). SVM will generate hyperplane in iterative manner, so we can minimize the error to produce better classification model.
- Support Vector
Support Vector is data points which are cloest to the hyperplane, Construction of hyperplane will depends on the Support Vector.
Hyperplane is decision line or boundary line which separates different classes.
Equation of straight line : Y = mx + c
So mathematically what is hyperplane
In geometry, a hyperplane is a subspace whose dimension is one less than that of its ambient space. If a space is 3-dimensional then its hyperplanes are the 2-dimensional planes, while if the space is 2-dimensional, its hyperplanes are the 1-dimensional lines.
In SVM when we have nonlinear data and need to be classified when we will convert the dataframe to higher dimension and place the hyperplane and bring it back to lower dimension.
To understand 👇
Margins, may be defined as the gap between two lines on the closet data points of different classes.
ϵ- Insensitive Tube:
Region or area between two line, any data point falls inside that region will be considered as the fallen class.