In the last lecture, we discussed how support vector classifier overcomes the limitations of marginal
maximal classifier.
It was by introducing a new parameter called cost and by allowing some amount of misclassification Merza
portrait to classify it still has one limitation support better classify as only work when the observations
are linearly separable.
We have discussed only Linnean hyper plint, but there may be scenarios where the classes are not linearly
separable, such as the scenario four point distributer like this by visual inspection.
We can clearly see that these classes are well separated.
If I can draw a circle here and say that anything within this circle is going to be purple and anything
outside will be blue.
That would be a much better classifier than the one on the right.
So to handle this limitation of linearity, we further generalize and use something known as Cottonelle
method to reach out support vector machines.
Using support vector machines will be able to draw nonlinear boundaries as well.
We'll be discussing support vector machines and the next video.