The maximum marginal classifier has two major limitations due to which it is not used in majority of
the deal work to Nadja's.
The first major limitation comes from the fact that we said we want to find a lenient hyper plain,
which perfectly separates the classes in predictive space.
But what if the classes are not perfectly separable?
That is, there is no such hyper plane which can separate these observations perfectly.
This is often the case in real world problems.
Suppose the observations are as shown in this graph.
Can you find any street lane that can separate blue dots from Purple Dot?
It is not possible.
Then how do we classify in this scenario?
We cannot do it using maximal margin classified.
The second major limitation is the sensitivity of this classify it.
Consider this an idea on the left.
We had a distribution on which we drilled this hybrid plane with maximal margin.
But notice, indeed, eight figure just that the introduction of one new point of hyper blink changes
immensely.
This is a completely different classify it.
Also, notice that Dick, new high powered plane will have a very small margin.
These are the points very near to the cyber plane.
So the margin will be very small around this hyper plane.
This is a problem because the distance of an observation from the hyper plane can be considered as a
measure of confidence that the observation was correctly classified.
Also, such sensitivity to one observation makes maximal margin classifier very prone to ordering.
Third, due to these two limitations.
We will move on from maximal margin classifier to support vector classifier, and we will see how support
vector classifier handles these limitations.