Helen, in all our previous sessions, we have learned what is a disease and how you can build disease,
and we have solved some of the use cases this last year that we have solved with respect to whether
the person, whether the student of different class and different gender is going to stay on or not.
We have solved this case using our our this approach.
Similarly, we have some use case regarding our appropriate information approach.
And we also learn what is pre brewing, what is postponing and indecision.
We are going to learn what that and what it is and what are cons.
And all of this is entry and what are different types of algorithms in decision.
Very, very first, let's understand different algorithms.
Let's understand our different algorithms.
Let's understand our different algorithms for this, isn't there?
There are literally tens of thousands of algorithms for this event.
Let's say you have heard about it, which is eighty three a.m., which is nothing.
My, my, I treated victimiser.
So it is what the algorithm to construct a decision basically for our classification problem.
It is basically for classification type of use cases.
It is basically for classification used this Heidi, used by news algorithm.
So how, how, how, how exactly.
It creates diffidently said basically uses a concept of information gain as a criterion to build up
this season.
And whereas if I were to say so, I did.
I did.
He always works, always works in terms of in terms of categorical attribution.
So whenever you have some categorical attribute, definitely go ahead with decided who internally,
whenever you will see that you have some cycad learn Monu and whenever you have some let's say here
you have a tree inside some module, you have something known as if you have two in this this tree.
This isn't reclass decision tree either classifier or the question, depending upon what you guess you
have.
So let's say I'm going to import my classifier because I have a classification.
So in this this season, tree classifier, if if we have this categorical attribute in our data, always
go with this idea tree, you can you can customize all this barometer's if you if you will, if you
will check all the different parameters in this in this class, you will find out you have you have
tons of parameters inside this.
And you can you play with that.
Definitely.
You can play with that.
So now what what what we have to do list it.
We have we have something known as we have something known as C 4.5.
So as far as we know, it is it is just for categorical.
But it is.
It is it is for continuous.
It is for continuous and for discrete data.
It is especially for continuous and discrete type of data.
So it is it is nothing.
But it is just an extension.
I can say extension of it is just an extension of this three and indefinitely.
It is much, much better than I did much, much better than 93.
It is definitely greater I against a much, much better than this idea here.
And it always deals with this numerical data.
It deals with numerical data as well as our categorical it is with both the scenarios, whether it's
a numerical use case or against this regression, or I can say it's classification, but it is it is
basically for classification and it basically uses our our Gini index.
Or I guess you can import the guinea pig to select our parent or to select our group, because in this
country the major concern is which in all is to be selected as a parent.
So basically it it internally uses internally, it uses Kitney beauty to build, to build, to build
up the season tree.
So basically Padoan Cuttone.
Carter highly was highly used against him.
So basically a third one is exactly you have heard about something known as God, which is nothing.
My classification.
I can see classification and regression you have heard about.
If you know a bit about this, you will definitely heard about this God.
So how is God?
Because this is basically so it also uses it also uses both in classification, both in classification
as well as in regression.
You definitely you can go with this card because it's a highly used algorithm in highly used.
So how how exactly it builds isn't internally, it uses something known as Guinea Index as as as I guess
as a barometer, as a default barometer to select the select rulebook as as how how I did select how
how my seat 4.5 five and select our our this type of approach, which is my guinea border post to select
Google.
But if you want to do modification, if you want to do modification in this, if you want to do some
kind of modification, definitely we can do that.
We can use Entropia as well in this in this in this card we can use we can definitely entropies definitely
we can definitely use this.
Similarly, we have something known as five point zero.
We have something see 5.0.
So it is definitely it is it is better than SI four point five, much, much better than this.
And it having however it has its own content does it.
It works with categorical data.
It works with categorical.
Target, I can say it works categorically, target, whenever your target variable or I can see whenever
level is of categorical feature, let's say yes no like that in that type of scenario.
This this highly used to the similarly we have something known as Musse that is not used that much,
rarely used Gore indeed in this decision tree, which is my thing, but multi variant adaptive regression
Desplaines I can see.
So what, what actually does.
So it always try to create a series of beer with linear model beer with a linear model.
Basically it it creates a series of pairwise, pairwise, pairwise in a model which is being used,
which is being used to modulate irregularity and interactions among all variables.
And this is not a widely used algorithm that's not highly used algorithm.
And this isn't yet.
But yeah, this guy and I did and see 4.5 definitely highly used.
And most other time, as in the Askia, what type of algorithm, you know, when you are going to build
a facility, will ask you whether you know this for twenty four point five, whether you know this card
or whether you know, IDC, what type of algorithm.
And we have many more.
Many more, many more.
Like we have something known as or this is a system we have something known as this isn't as dumb as
that.
So it is nothing.
But it is just one label.
This is it.
And it is used for generating a decision tree by just a single.
We have to just do a single spit.
That's it.
That that's all about the.
And we have something known as something on it.
So what, how this works.
So whenever, whenever you have you have much analyzing data or I can say you have outliers in data
whenever you have some.
I can say that say you have some small little X that you have some small dataset.
Yeah, I have I have worked with I have worked with this algorithm.
I work with this type of algorithm.
Yeah.
So whenever you have some small data and let's say that, that that data has outliers and that has some
noise kind of thing, go ahead with this and five tables and just go ahead.
Would definitely you seem a darn good accuracy when you go ahead with this and 5000 of this of this
algorithm.
And we have something known as is there are lots of things like this, nothing but just my conditional
investigations, all sorts of Elgort overbuilding, incidentally.
But but yeah.
But at least you should know, what is this ADT?
What are my card algorithms?
And, you know, what is the C point C for five.
And and if if you are OK, then you can consider this and five and see 5.5.
I will, I will recommend you at least at least you should know this to at least at least you should
know.
And if you are OK with this, definitely you can consider this too as well.
But but actually try to learn this to try to learn because if if if you really want to.
Good.
If you really want to go into into machine learning, if you really want to go into machine learning,
do definitely you can you can see that all this type of thing, all this fight at least at least you
have to go through this fight if you want to.
If you really want to go with machine learning and you have something known as this security, which
is nothing which which works some dumb thing on a statistical test with something on Chi Square test,
which is nothing like this, might chi square automatic interaction detector dexa of algorithm for to
build.
So let's talk about what are the consent forms of the season.
Let's talk about, let's talk about.
So let's, let's talk about what very fourth quarter and one day this or this isn't.
So the very first is it is used both in case of regression as well as in case of classification.
That's the very first one is definitely and the very first and very second one is exactly very easy
to learn.
It's very easy.
It's very damn easy to learn because nothing you can just done buton Loades, just entropic type of
things and you have to learn something.
One is getting Gote, but just you have to learn to two things, just just two things to learn it.
And the rules of splitting is clearly mentioned as as as we have learned how to create this.
Isn't we using you know, we have learned this using this skinny necks, this entropy information gain
like I never told anyone tases.
Exactly.
We don't have to deal with outliers.
We don't have to deal with.
Outliers in case of this season, because this isn't is exactly what's on the basis of this kind of
rule-based thing.
So whenever you have outlier, it doesn't get impacted that much.
Similarly, similarly, you have something known as is killing.
You have heard about this is killing and something of this normalization, something known as normalization.
So there is no need to perform.
This is scaling and normalization.
There is no need to perform.
This is scaling and normalization in case of this disease and in case of disease.
And there is no need to perform this as as this disease and treat as a disease.
And three entirely works on the basis of the season that greater than these whether it's less than these
is so there is no need to do is killing and not for that are exactly my all the advantages of this.
Let's talk about let's talk about water.
What are some this I don't want this.
What are some discussion on this?
Let's talk about this for the very first tell that I have filled out in my walking with this algorithm
in something some real world scenarios.
So what if we are going to make let's say, if we are going to make a small change in data, let's say
we are going to make some small change in let's say, in creating data, not in data.
I can see it as training.
You do so whenever we are going to make a small change or a minor change in this training data, what
it will do, it will cause on a stability.
Or if we make a small change in training data, basically it will shift the node.
It will shift toward, let's say this is DLT.
So if we do a small change, if we do a small change, it will let's say this is my entire decision.
Let's say this is my let's that this is my own time.
So if we make a small change, let's see over here, I have made a small change.
So it will make a shift of it will make a shift of this this node, this node from one side to the other
side.
And many other reasons that this is not a disadvantage that I have faced, that I have faced when I
was working in some real world scenarios.
And definitely one more advantage is definitely a problem of all this is very, very high, is very,
very high, is is very, very high, literally very, very high in case of encephalopathies tree.
So we have something known as pruning tool to to get rid of this autophagy and tired of it and Tadamon.
Exactly.
It takes more time to train and this isn't a model.
Definitely takes more time because it takes more time.
It takes more time to Crenna.
This season, Trimodal, it definitely takes more time to clean and remodel than under classification
algorithms like like autistic and many others, because as we do a lot of spreading, we have one here.
Obviously, we have lots of earsplitting or here or here or here.
We have lots of it spreading out here and lots of calculation if if I would consider entropy.
So we have to consider entropy and let's say information again.
Then we have to do lots of also splitting and we will end up having the misandry.
That's why that's why it will it will take more than anything.
So that's all about the session holtsville of the session?
Very much so.
In the upcoming session, we are going to learn how this essentially works, actually, in case of regression,
because we have learned how it exactly works in case of classification.
Whenever we have some discrete data and whenever we have a continuous data show in the upcoming session,
we are going to learn how how actually it works in case of some some regression you.
So we have to understand as well this.
So that's all about it.
I hope you will love this very much.
Thank you.
Have a nice day.
Keep learning.
Keep growing.
Keep practicing.