Hello, all in all, our previous session, we have learned much, much about disease and we have learned
entropy information gain broadening and different parts of what are taught at conferences.
A. What are different types of algorithms for disease?
And we have learned much, much some use cases regarding that.
So in this session, we are going to learn how this season works, how this season evolved actually
in case of regression.
And we have to consider some use as well as selected.
I have some let's say I have some features that have some and a number of likes.
It like to have some three features, x1, x2, x3 and let's say I have some target variables that I
have some target variables that we have to predict.
So I have some data as Lexia for let's say this five, let's say the six to seven, let's say it is
nine, let's do this again.
So similarly one had x2.
I have something hundred let's say then let's say this for the dyslexia sixty.
Let's say this is 70, that is is 200.
Similarly extra effort.
I have something 70, 80, something 90, something like 60 is something 40.
Similarly something I have twenty one single target.
Very well.
Let's do something.
Let's say at this point, let's say it is point to let's say this point and let's say it is something
you can consider, let's say some values you can consider, let's say it is pointed and let's say you
have some new detail, except let's say this is a new reality.
This is my new data series, my new data and using using you.
This is an algorithm.
You have to predict what can be your target features, what can be your target features.
You have to target this.
So how how how you can predict this using your decision tree in case of regression.
So how how you can prove this.
So you will see what here you will see or hear this x1 this x1 actually range between four to ten and
this actually ranges between definitely one two five hundred something.
Similarly this extra, similarly this x ray the somewhere between nine nine two hundred, some very
content I can see and what we have to do, we have to split, we have to split or let's say let's it
let's say I have to split, let's say I have to split my column this extra exit.
I have to split this column x2 with the range of one two hundred one two hundred.
Let's say.
I'm just assuming I'm assuming that I would split it.
Let's say the very forced changes.
One, two.
Twenty second changes, 20 to 40.
Let's say this ranges 40 to 60 Aleksa the last one is exactly sixty two hundred it this is how I split
it let's say I have just assuming it, I just assuming so if I have to right.
Range, if I have the right range let's say if with respect to x1 if I have to split it, let's say
the first block I will do this pretty one to five to six to ten.
The second is similarly with or x2.
I have a split it in various blocks.
One, two twenty block the very first one.
It's in the second block you can consider or if you at all, if you want to let it split, it allows
you when you will consider use case you have somewhere between five hundred let's say if you will consider
it is five hundred.
So you can see as it is, let's say the first one is one two hundred, the second one you can remove
this, you can do all this thing.
Let's say I'm just going to consider three splits, not more than three.
That's also going to remove the split second one is exactly my 100 to 200 and it is definitely 300 to
500.
Let's it this is my all these things.
So it will definitely be 100 to 200 and 300 to 500.
These are all the splits.
Similarly with X3 from the limit X3 as well, except for this one to thirty, then I have something
to do.
Sixty then I have something sixty two assuming two hundred that these are a number of splits number
if I can see blocks, number of blocks.
Four different different features.
SDMI blocks for different different features, let's say.
I have to predict for what if this one is.
What if this extra is 200?
What if this X is 20 minutes acappella before this?
So you will see this five.
This fight lies in this quadrant, whereas this X to this 200 is 200 lies in this quadrant, whereas
this X three where it is 21, it will definitely lie in this in this quarter.
So now what we have to do for this X1 block, we have to we have to compute the average of Y like this
average survival.
Similarly, with with respect to this, with respect to X2, with respect to this block, we have to
compute average of, let's say divide to.
And let's say with respect to this blog, we have to compute average of that.
This is Whitey and then we have to simply compute to me.
It's me, simply compute.
It's me.
And it will.
It will exactly.
It is.
It is not what it is.
It is three that it is.
Yeah, so we have to simply compute its mean, and this is exactly my final prediction for this data,
for this data set, for this dataset, for this and this is a final prediction that I've achieved now
of caution that will come across your mind how how I have selected this region because I am just assuming
more here.
So how how I have telepathy to actually open a new page.
Let me open it.
So basically, we have to select our region.
We have to select our region in such a way that it will basically reduce its some of its growth.
Some of the girls at us or against some of us, Carol, residues, so you have to select your regions,
your little regions in such a way that you thumos is cleared of errors, will be your minimum, will
be your minimum.
Do you have to select your region?
Is Chubbie likelike in case of little regression?
You will observe whenever you have them.
You have to have some regression as let's say you did upwards and you had to select that line, that
line so that your residues with nothing might add to what what exactly that at think what is at a ATRs.
Nothing but my what I have predicted.
What I have predicted and what is the actual value and what is the actual value?
So this is exactly what.
So you have to minimize this error in in your linear regression.
So basically, you have to minimize this error in your linear regression algorithm, using using something
known as gradient descent approach, using gradient descent approach, you have to minimize this error.
Now, how to minimize this error in terms of the season three, in terms of the season trip.
So we have a simple equation for this.
We have a simple equation for this.
That is not what a simple mathematics.
We are aware of my gym, nothing but one to G, I will I would talk about each and every item where
I belong to each and every teacher.
And here it is nothing but why I this why I gap the oddest thing.
One reason Hawley's get so this is I can say this, this, this orgy is nothing but my are one reason,
one reason g so these are all my reasons that we have to, that we have to consider, that we have to
consider for each and every future and, and that region are selected in such a way that our that our
adults or I can see some of ATRs, some of of some of glad of that is not not at all.
It is just a sum of square of arrows will be your minimum.
So whenever this entire calculation, whenever this and calculation will be minimum, will be minimum,
then we will select those regions as my we are this why I this why I let let me let me talk about this.
What is this.
So it is nothing but it is just an average of why for a particular reason for a particular region.
What is an average of fly.
And this is roughly what your exact average and that is nothing but your your your I can say about your
submissions on Jaquiss to one a number of reasons.
You have the simple solution that a simple mathematics, simple mathematics.
Just just do have to explain this and you will get your reasons.
So who Lavoipierre.
Who's peer.
I can say give your gifts.
You this, this, this calculation, I this calculation, wherever this calculation is maximum.
So we will select both reasons because this calculation is maximum when this sum of a stack of errors
will be minimal.
The death is simple calculus, a simple.
So that's all about decision hope do.
You'll love the entire decision reappropriation very much and hope you are very much compatible.
This decision Griezmann.
So thank you guys.
How nice to keep learning.
Keep sharing.
Keep getting.