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Support Vector Machines (SVM)

considered a combination of K Nearest Neighbours(KNN) and linear regression models

mostly used for classification, but can also be used for numeric prediction.

SVM tries to separate out the different categories of data by creating a flat boundary called 'hyperplane'.

SVM tries to find out the Maximum Margin Hyperplane(MMH) that separates the two classes with maximum distance.

Hyperplane is a flat surface in the N dimensional space.

SVM requires all features to be numeric.

what is a support vector?

support vectors are the points from each class that are closest to the Maximum Margin Hyperplane(MMH).

each class should have one or more support vector.

an important property of SVM is that just by the support vectors, it is possible to define the hyperplane MMH.

in linearly separable data, get the shortest distance between the points of two classes, perpendicular line is the MMH.

How it works

if data is not linearly separable, we allow some data to have wrong class while maximizing the distance after adjusting the penalty for wrong classification.

another way to do it is using a kernel trick.

kernel trick tries to transform the non linear data into linear data by viewing it in some other dimension.

adds additional dimensions to the data, and can learn the relationship between dimensions that were originally not stated.

Pros of SVM with non linear kernel

can be used for classification n regression.

noise doesn't affect it much, doesn't overfit.

easier than ANN

used a lot in image processing/recognition.

Cons of SVM with non linear kernel

need to test with a number of kernel and model params to find out the best model.