Support Vector Machine (SVM) is a relatively simple Supervised Machine Learning Algorithm used for classification and/or regression. It is preferred for classification but is sometimes very useful for regression as well. Basically, SVM finds a hyper-plane that creates a boundary between the types of data.
SVM has the penalty parameter which controls the trade-off between minimizing the training error and maximizing the classification margin. Moreover, kernel parameters determine the distances between patterns in the new space, dimensions of the new space, and the complexity of the classification model.
Advantages of SVM:
- Effective in high dimensional cases
- Its memory efficient as it uses a subset of training points in the decision function called support vectors
- Different kernel functions can be specified for the decision functions and its possible to specify custom kernels