So first, we are creating a random forest model.
So from a and symbol, very important random forest classifier and grading, boosting classifier.
So what is random, first class of random footage classifier is a supervised learning algorithm, which
is mainly used for classification problems.
As we know, that forest is made up of trees and more trees, random forest on them creates decision
trees on data samples and then gets the prediction and finally selects the best solution by means of
voting.
It creates many trees and different trees, gives different answers.
And using voting, it's the best possible answer.
So first we had initializing random this classifier and storing it in model.
That's the cell.
Then the lifting of a random forest model to extreme vitrine, let's run the self.
So to check the parameters, you can shift it up so it will show you all the different parameters which
are used in random forest.
So you have initializing the model, you can check it here, you can.
So you'll find all the different parameters so you can modify any barometer if you want.
Let's close this.
So we have switched over to role model an next.
Now let's put it on our test to take.
But the model that predicted so model is a random forest, so using that predict function, they're
predicting it Anderson.
Of that is good inaccuracies, converting to barometer's whitest and white predicted.
Let's run this on.
So here we got an accuracy of fifty eight point seven percent with random forest, classify it.
Let's proceed further.
Now let's create Grenadian boosting morale.
Gradient busting is a machine learning technique for classification and regression problems, which
produces a prediction model in the far off and symbol of the production models.
Typically like decision trees.
The logic behind creating the posting is simple, it works on basic assumption of linear regression.
That is the sum of its residually zero, the residual should be spread randomly around zero.
Now think of this residual as mistakes committed by our projector model Aalto.
Three best models are not based on such assumptions.
But if you think logically about this assumption, we might argue that if we are able to see some pattern
of residuals around zero, we can leverage that pattern to fit the model.
So the intuition behind posting algorithm is to repeatedly leverage the patterns in residuals and strengthen
a model with weak predictions and make it better once we reach a stage that residuals do not have any
pattern that could be model.
We can stop modeling residuals, otherwise it may lead to the.
Algorithmically, we're minimizing our loss function such that the best loss reaches its minimal.
Let's proceed further.
So here we are creating a list of the world's tallest.
And here we are storing learning rate.
So then we're creating a for loop for learning there in a long list, you just I.
So here we're taking a different learning rate in our classifier, so and of course, is equal to Gregan
boosting classifier and number of estimate, the learning related to learning rate, which is this one
first in love, first me is this one.
Second is this one.
So Max is going to do Max.
That is going to do.
And random street is called zero.
So you're going to see a live dogfighter, extrem come little.
So first, very initialism the classifier, and then we are putting it on extreme well-trained.
With different learning rates, let's say.
So here first we're printing, learning read.
And then we're printing, training, accuracy and validation accuracy.
So with that format, GBE underscore selectors call extrem.
So left score gives us accuracy.
Anderson.
So during training, we got an accuracy of zero point 60, which is 62 percent.
And in addition, we got an increase of 68 percent.
Now, let's create a moral.
Exhaustible started in summer learning method.
Sometimes it may not be sufficient to rely upon the results of just one machine learning model.
So and so we're learning offers a systematic solution to combine the predictive power of multiple learners.
The models that form the ensemble, also known as beastliness, could be either from the same learning
algorithm or different learning algorithm, bagging and boosting all too widely used and simple learners.
So here in this, we will be using boosting technique.
Let's just import export classifier from Zervos.
Lecture the.
And then initialise Juba's classify it.
In exuberant, you left and then so exuberant, dogfighter, too extreme and Vitron, let's run undersell.
Now, squad is called to its CB and see if that's the extent of it.
So this will give us the accuracy score.
Let's bring this.
So here we got an accuracy of sixty two point seven percent.
So comparing to other algorithms we got most accuracy with, you know, let's try supporta machine.
Support vector machine is a supervised machine learning model that uses classification algorithms for
classification problems.
Support vector machine creates a support line.
And it creates a support line, so.
The points above the lines are yes and points below these lines are no different in that we.
So let's proceed further.
So if you want to read more about this, you can click on this link.
So first import.
From Ashkelon, not as important as we see, so to classify them, we're initializing as we see.
With Random Street is called a zero, and Carneal is called Alabi of.
And a story you didn't classify it.
Then we are fighting it on Ekstrand and Vitrine, so classified outfit, Extreme Camo Mildren.
So the parameters of equity just shifted to.
So we have not reported it yet.
That's why it's not showing just shift in the.
So they have successfully.
We deliver a service immortal, so you shift it into all the different parameters and SBC.
And you can also read about the new default parameter.
Like Col's here, the default rate is above.
And you can use linear poly sigmoid.
Etc.
Now, let's close this.
Now, let's predict on our test said.
So I predict basically those classifier that predict that.
LZ Granderson.
Now, let's check the accuracy of over.
So to classify, LZ Granderson.
So here we got an accuracy of 58 percent.
So.
We have implemented artificial neural networks at random for this classifier, creating the boosting,
boosting and then support the machine.
So out of this, we got very good accuracy with a boost and also we got a very good accuracy with artificial
neural networks because in artificial neural networks, we have not performed any future engineering.
Still, it was working really good.
Even though we got a very good accuracy, but this can help the banks in knowing whether the customer
is risky or not.
That's it for this country.
See you again in the next project.