SVM

Support Vector Machine

- Supervised Algorithm, where one need to find a hyperplane that best separates two classes.
- It tries to maximizes the distance form the nearest points of all the classes.
- It Used with Classification,Regression and Outliers Detection.

Variations of SVM

-- Linear SVM used with linearly seperable data
-- Non-linear SVM used with non linearly seperable data.

Kernel Trick

  kernel trick is widely used in the SVM model to bridge linearity and non-linearity.
  It allows us to operate in the original feature space without computing the coordinates of the data in a higher dimensional space.

Kernel Functions

  Linear
  Polynomial
  RBF
  Sigmoid

Additional Resources:

https://shuzhanfan.github.io/2018/05/understanding-mathematics-behind-support-vector-machines/

https://scikit-learn.org/stable/modules/classes.html#module-sklearn.svm