Hello.
So before we're in deep dive into the session, let's have a walkthrough on all our previous session
that we all have done with respect to Ken.
So basically, we have this huge case how we have to solve this thing using concept of Euclidean distance.
You can come up with this new data point basically belongs to this this this class one.
This is circle class.
And then we have also got some basics regarding this canid how to compute.
This man had then what is exactly Euclidian.
And then we have also learned why this Janet is a lazy learning algorithm.
And then we also cover up why this Ganon is not recommended if you have some huge amount of data, let's
say if your data is in some big data framework's in some Apache Hypertrophied or Blixen in some Hadoop
clusters.
So at the time, Ganin is not recommended then.
We have also because because at the time that that entire data is considered as a model itself.
So it makes no sense at all having such a complex model.
So we have all covered this in all of this.
So in this session, what we have to learn, we have to learn basically how to compute, how to compute,
basically distance.
Between let's see what if we have a categorical data in our use case, how to compute distance between
categorical data, how to compute?
Basically we have something known as Heming Distance.
So using this, we can come up what exactly?
The distance between our categorical data.
Let's say let's say I have some let's have some categorically say I have some categorical data, say
A, B, C, D or two.
This is write down A.
This is my E, EDF say this is this is the entire thing.
So if I will see you how you can compute distance between distance between this and distance between
this, this, all these things.
So in case of categorical, we try to check correctly my character, basically character by character.
So you will see in this A2A it is, it is the same.
It means it's, it is zero.
Similarly, Betawi, they are different segments, its values one if similar than zero, if not similar
than one simple.
So one and one over here then what we will do then using this, having this tenth what I'm going to
do it says summation of.
All Vaughn's summation of all one.
Where you're X doesn't equal to Y, that's what this having distance will see.
So if I have to write it mathematically that I can say it is nothing but submission of equals to one
to bill and still the number of characters I have, the number of characters I have and submission of
all the ones I can see where my X doesn't equal to Y.
That's what this is, having distances, some simple distance and nothing but just one one one three.
Simple.
So I can see the distance between this and distance between.
This is nothing but three.
So let's talk about a very basic justis where we can use this having this dense concept.
So let me open a new page very first opening.
Suppose suppose I have here some data to support supposin class one support.
This is data of cluster.
And suppose I have somewhere have some data points of class to support here, I have some new data and
on the basis of that, I have prepared get it belong to class, to a class one that we have to some
of the data of class one is let's say let's say evictee, let's say ABC.
So this is my other data and let's say ABC.
And let's say ABC, this is entitled Classon, similarly, I have some data with respect to X, Y,
Z, A similarly Oread, I have X, Y, be similarly over here.
I have something X, Y, a similar law here I have something X, Y, a, yeah.
So whereas in class two I have all this data, this is only two plus one.
So let's say I have to predict four.
I have to predict for this X is Sini belongs to which class it belongs to plus one or whether it belongs
to class two.
We have to protect this then.
Then when my guess is three it means, it means I have to search for three nearest neighbors.
Then once we have three nearest neighbor, then we will consider the probability of the higher count.
What I can say we will go with basically Mesabi.
That's that's what is the exact meaning of this goes to three.
So in such cases, in such cases, whenever I have this this justis, I can't consider my Euclidean
distance because this is my categorical data.
In such case, I have to go with my having distance.
I have to go with this having mister.
To me it is this.
Yeah.
So what.
We will do it here.
We will compute distance between distances will compute distance between this, this distance, between
this, this distance between.
This is similar here we have to compute between the distance, between this distance, between this
and similarly we have to compute distance between this.
So let me come to a distance.
So basically over here you have two same terms, basically.
So distance would be nothing but two.
Similarly, you here I have just a single thing, which is exactly.
Which is why I see.
So here my t similar here I have distance as fort because tachometer now the common character I can
see similarly here I have this data three similarly warrior I have three.
You will see I have just a single common tongue which is exactly X..
Similarly what here.
I have three here.
I have two here I have again two.
Here I have again.
So you will see, you will see two nearest neighbors to Nusi.
Both are of basically class two.
So, so what we can do here so we can see, we can see this X is as, as X SCD has two nearest neighbours
of class two because you will see if the distance is two is distances two.
It means these are my two nearest neighbours.
So it means, it means this X if this X is belongs to class to.
That said, your faith, your statement gets of sync, but you have to just come to the stance, that's
it.
So that's all about the situation.
In the upcoming session, we are going to learn what exactly are the real life use cases of Jennet and
what is pros and cons of Ken.
And we are also going to learn about this.
So that's all about it.
Hope you love it very much.
Thank you.
How should I stay?
Keep learning.
Keep growing.
Keep dancing.