Support Vector Machine (SVM)
Description
Support vector machines is a machine learning classifier algorithm used to classify the class membership of an unknown based on training data.
Simple Explanation
Fig. 1. A simple support vector machine marks a linear decision boundary (magenta line) between the x class (blue cross) and square class (green square).
During training, SVM learns from the data to draw a decision boundary that separates different classes. To ensure optimal separation of the different classes, the decision boundary created has the maximum distance between it and the training examples of different classes.
Kernels
Fig. 2. A support vector machine marks a nonlinear decision boundary (magenta curve) between the x class (blue cross) and square class (green square) with the help of kernels.
The simplest of SVMs can only draw linear decision boundaries (Figure 1). But fortunately, with the help of kernel functions (addon to SVM) and some mathematical tricks, the SVM can compute more complex nonlinear decision boundaries such as the one shown in Figure 2. See parameters for more details.
Parameters
Parameter  Description 

C  penalty for misclassification

Kernel  MLGenius currently supports:

Degree  (used in polynomial kernel) higher degrees will generate more curvature in the decision boundary 
Gamma  identifies the radius of influence a single training example has

SVM type  while SVM has been used to do more than just classification, MLGenius currently only supports the classifier version of SVM 
MLGenius will help you determine the best parameters to use for your dataset
Pros and Cons
Pros  Cons 


