Halaal, before going deep dive into the session, let's have a quick recap of what we all have done
in all our previous session.
So basically we have learned some basics behind Kenen why to use and when not to use.
Yeah, we have definitely learned not to use this and whenever you use data.
So definitely it would take lots of time.
So it is always suggested to data scientist now.
But you can and whenever you have huge data and we have also learned we have this use case as well that
we have to solve in this session.
And then we have learned the basics behind what is your.
This is nothing but a distance between two points.
And then we have learned something this this this use case, how Google Maps and all logos use the Manhattan
business to compute distance between two places.
Then we have learned something.
This is a very basic idea behind what this method understands, which is nothing but the summation of
this to us.
So this is exactly the case that we have that we have to start in this over here.
So basically, we have learned, yeah, we have to use a concept of Euclidean distance to compute distance
what our data point we have with respect to all other data points.
And here, let's say my values, K3, it means we have to find clean nearest neighbors with respect
to this data point.
Let's say I going to compute distance between each and every data point, let's say, from this today.
Similarly, the similarities in the similarities, similarities to all the data points available over
here.
All the data points, all other data points.
I'm going to calculate distance here.
Let's say let's say the move on these things.
Yeah.
So let's say the three closest data points my eye can see.
Let's say that this is my first away.
That is very much good to and this is my second.
Alexis, say this is my this is my third point.
Let's say after calculating this to let me say, after calculating this death, we will try to select
it up.
We will try to select a point which are exactly nothing, but which is nothing but my two circles,
my two circles.
And one of this this is greater level than what we have to do.
We have to basically compute probability of new data point with respect to with respect to this circle
and with respect to this with respect to this.
This is that ship level.
So what we will do, we have to see what is our probability of new data point, which is exactly this
diamond shape.
With respect to this class, with respect to.
With respect to this class, we have to compute it, so probability will be nothing, but I can say
to white hope because you will see it's going to stop and total total datapoints or against the total
shift at three.
So it means it probably will be nothing but just just two or three.
Similarly, similarly, with with respect to this this level, I have something one by will observe
my new data point, which is Dayman ship has a highest probability with respect to this this circle
ship.
So it means it will belong to this class.
It will belong to this circle class that I can see.
Let me open the previous page.
Yeah, I can see.
I can see.
This is this data point.
This data point belongs to belongs to this circle ship plus four.
Definitely.
CNN is highly used for whenever we are looking for similar items.
Let me open a new page and let's discuss some some characteristics of Ken.
Let's discuss that.
Discuss.
So basically, basically this Giana you have heard about, this canon is lazy learning algorithm.
So what exactly is the meaning of this this lazy loner concept?
So we all have learned some basics or some basics of machine learning algorithm like like what is linear
regression?
Let's say what is decision tree and let's say what is random forest and what is logistic regression.
So in all these algorithms, what we will try to learn, so we will try to learn basically some relationships
in our data.
But but but with respect to this, Gernon but with respect to this canon, it is not happening.
So so basically, basically in this kenen in the scanning algorithm, unless and until you are not looking
for a prediction, you are not looking for a prediction unless and until you don't have you don't have
some testing data or you don't have or you don't have some unseen data and can and will never try to
learn relation from data.
What I can say, Kinen, this algorithm will never try to build some kind of structure from data a little
bit.
You don't have some test data unless and until you don't have some some unseen data.
So basically, until unless you don't have unsign data, Kanal will not do anything that that's why
it is known.
And that's why this Donat Lazy Learning algorithm or or I can see this, this algorithm that as you
will learn the basics of what is linear, what is, what is the decision.
So they are trying to build some kind of model.
But if I will talk about the scannon it now tries to build model it now or tries to build models of
what it will do, it just calculate or I can see it, just try to check, distance it, just try to
check the distance between your new data, whatever new data you have, or you can say whatever and
see that you have.
And basically on based off this distances, it is able to give you some kind of prediction because you
will see in our use case, we have used something known as Euclidean distance for basically using this
Euclidean distance.
It is going to give you some kind of prediction.
Yeah, it belongs to this class.
So basically, I can see basically in this canon, in this can it in this algorithm, my entire dataset,
my entire dataset itself itself is a model, my entire dataset itself is a model.
So if my data would be very huge, then model will be huge and complexity would be huge and it will
take more time to do prediction.
That's why that's why this canon is not advisable.
If you have if you had huge amount of data, let's say let's say in terms of Greece, in terms of let's
say at terabytes or in terms of petabytes or you have some huge amount of data in some databases collected,
your data is stored in some of the big data frameworks, let's say in some al-bakhit.
Let's Appadurai, what about a bit?
So whenever you have such a huge amount of data at that type of scenario, this Giana isn't recommended
because this entire data will be considered as as a model.
So if you have that much complex model, it makes no sense at all of this model.
That is why it is not recommended whenever you have huge amount.
So that's all about the session in the upcoming session, we are going to learn how exactly we can compute
distance between categorical data and we are also going to learn how this cannon is used in case of
regression and different expect as well.
So that's all about it.
Hopefully you'll love the session very much.
Thank you.
How should I stay?
Keep learning.
Keep growing.
Keep practicing.