SVM (Support Vector Machines) Modelling

• SVM is a linear regression model
• Helps in dealing with large volume & high dimension
• Not suitable for imbalanced data (why?) 
• SVM on imbalanced data often produces models that are biased towards majority data and hence poor performance on minority data.
• Tips on choosing the right type of kernel for SVM
• Ultimately boils down to trial and error
• But not completely random, lot of this is informed by experience
• You would get a sense what the classification boundary would look like when doing the EDA
• If you find that data plot is completely intermingled, then non-linearity is needed.
• If the data points are reasonably separable by a linear hyperplane, more or less, then linearity should do. 
• If there is a reasonable curvy line you could draw as a separator, then a polynomial kernel should work.
• If the data is hopelessly intermingled, you would need a complex kernel. A most common choice in this case is an RBF Kernel.
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SVM Notations

• 2-D hyperplane (essentially a line when it comes to 2-D) equation
  W0 + W1X1 + W2X2 = 0
• n-D equation
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