Hello, let's talk over what is Random Forest, which is exactly a machine learning algorithm, but
before going deep into random forest, let's have a quick overview of our previous session in which
we have learned your random forest nothing.
But it is just a collection of multiple pieces.
And then we have also some knowledge behind what is this is a tree, which is exactly this nothing.
But it is just a hierarchy like structure.
Then we are also learn.
Yeah.
What are the main key factors using that you can build.
Believe this is a..
Which is exactly.
Information gain and deniability.
So whosever future has highest information that will get selected as a pattern or because a major goal
assign the season tree is nothing.
You have to select what future as a pattern or similarly using Guiney index as well in your body as
well.
Hocevar feature has lost in index.
It means that particular feature has lowest inside it.
So it means I'm going to select that feature as my better note that what my index will do.
So using either information or Guiney, you can select your decision tree and once you have a decision,
you can easily do prediction.
So intersession what I am going to do.
I'm going to give you our intuition behind what is exactly a random forest, which is nothing, which
is just a collection of which is just a collection of multiple decision, which is just a collection
of multiple pieces.
And so out of is nothing but random forest is nothing, but it is just a collection of multiple pieces
and trees.
So inside the forest this season, trees like this is my decision, given that this is my decision to
do and let's say this is my decision tree.
So decision tree, which are big over here that are basically built using a technique known as bagging
on, you can also see both estab aggregation because also dumb as boot is trap.
Aggregation.
Would this tap aggregation?
So what exactly is this Baghi or what is this step execution?
So bold step execution or bagging is nothing good in which we are going to create a multiple bassnectar.
This is my Bhagavan, this is my back, and this is my back treat.
Let's say this this is a free one.
Gives us some protection for some data.
Let's do one.
Let's say this is zero Alexiev.
This gives one for this.
What this bank will do.
It will exactly go with the Mesabi or I can see it will basically aggregate my prediction.
So once I will aggregate I will come up with a final prediction as one because Mzoudi always wins or
Hennesy Mzoudi goes with one.
So you will also over here.
This is exactly my Baghi techne.
So let me consider a very simple use case order.
Let me open a new page.
So I'm just going to open a new page.
Now suppose suppose I have my entire dataset.
This is my entire dataset.
Suppose this is my entire dataset that has some support and columns and angles.
This is a dimension of my dataset.
So what exactly initially will do so initially, what rhinovirus?
Billu.
Initially I will pick up some sample.
Let's see.
I'm going to need to sample one, so I will pick up some sample of data and I will pass I will pass
this to my model, this to my model, which is exactly this is and which is exactly like this one.
So I will pass some sample of data with roll sampling, with roll sampling, as well as feature sampling,
road sampling and feature sampling.
Or you can see that all sampling and column sampling.
So what exactly is this row sampling and sampling support?
This dataset has six hundred rows into its and column.
So initially, what will happen initially?
So what will happen next in sample one, let's say two hundred rows and three column will get shifted,
let's say randomly, randomly, so let's say randomly, two hundred and three column will get transferred
to my this isn't one model and it will do some kind of prediction on this.
Let's see if it gets prediction, let's say having some regression use case so it will give prediction
in the form of subcontinent's data.
And if we have a, let's say, classification use, it will give a prediction in the form of discrimination,
either one or either zero.
And if regression, it will do some in the form of continuous nature.
Let's say ten point eleven.
Anything else?
So this is with respect to my sample.
Similarly, after it, after what will happen, whatever data I will have there, it will get back to
my data set.
It will get back to when you does it again, my sample to my sample to withdraw all sampling and future
sampling will happen.
And again, this data will get over here and my decision G two will get trained on this particular data,
on this particular sample, and it will do some kind of prediction.
Let's see if regression is good.
So I am going to predict as athleticism some value and if classification uses, I will predict in some
discrete nature.
Similarly, after some rule stopping and features happening again happen, then I'm going to consider
my sample three.
I think this is my decision.
This isn't it tree.
And this season the three will get clean on this particular sample, on this particular sample, and
it will determine exactly you prediction as well.
So it will exactly give a prediction as well, depending upon what use case we have.
So what will happen here?
So as we know, as we know, that we will open a new bid.
As we know, so as we know, this country has basically a property, it has basically a property that
it has high variance, that it has high.
So what exactly is the meaning of this high variance?
It means basically there is a huge difference between what exactly my prediction is and what is the
actual data.
That is the exact meaning of variance.
So let's say.
On training data at sea, on training data.
Next year on training data, you have some good accuracy, let's say your accuracy is somewhere around
97 percent.
So it means it means my decision and my decision is able to learn relationship very properly.
But when I have some unseen data, but I have some test data, then I have some test data.
So it will not give me that much good prediction because what this country will look like, like this
is nothing, but it is just having some kind of season.
But what if.
I have a data that has that has something of very outlier or I can see that is far behind, that is
far beyond the range of the any one note of this.
So it will basically give us the lowest score, 60 percent in case of test data.
So this is basically my variance is in case of Triniti, that you have high score, whereas in case
of test data you have to score.
But what random will do?
It will convert this high variance, this high variance into law, various.
But the question is how how exactly this random forest will convert this high variance in blue areas,
because you will notice, if you will notice that my decision tree one will get green on some particular
data.
So it will basically become expert for this particular subset.
So it will basically give some kind of prediction, that decision to give some kind of prediction.
And then season three will get very do some kind of prediction that we have our final prediction that
we have a final prediction.
So basically each and every decision will get trained on specifically some kind of data.
So they basically become expert on the data.
That's why they convert.
That's why they convert this high that is into low variance.
So this is basically a main characteristic of random forest that it can load that high variance which
caused by this is entry into this low variance.
So let me open a previous page.
You will see what here you have decided to do season three so that our let's say you have a regression
use case.
So what RF will do?
What I will do, it will consider mean of all the predictions given by this one.
So what it will do, it will consider mean of devotion to duty three by three.
So it will basically basically consider mean of this, whereas in case of classification it will basically
go through it already.
Or I can say it will consider more of all the prediction.
Whoever has the highest count, Marella Forest, will consider it as my final prediction.
That's what happened in case of classification.
In case of regression, it will basically consider me of the interpretations and interpretation if I
talk.
So if I will talk about interpretation.
Right.
So it is difficult to plot then the forest?
It is difficult for us because it is nothing but a collection of multiple decision trees.
And you will see in this season, if you have one decision, you can easily interpret it, you can easily
interpret it.
If you have to block it, you can easily block that decision using some libraries of some libraries,
using some plantings of export.
Underscore this, which is exactly a library in Biton.
And by doctors using this using both this one, you can easily plot your entry, but it is difficult
to spot because it is nothing but a collection of, let's say, hundreds, and this isn't free.
So it will become a little bit difficult.
It will become politically difficult to interpret your forest.
That is, if I can say that is a drawback of random forest.
So that's all about the session.
Hopefully you will love the session very much.
And my easiest way of explaining the forest for you.
I hope you will love the session very much.
Thank you.
Have a nice day.
Keep learning.
Keep growing.
Keep practicing.