All right, everyone.
So another machine learning algorithm, which we are going to play on a same spam message classification
dataset.
That is a support actor machine.
So Ledger's a plate.
And let's try to value it with the same classification criteria like accuracy score and confusing rhetoric.
Center Classification Report.
Let's try to generate so seem like earlier on live we are going to use it, but now instead of random
fighters, it will be a support racked up machine.
So first, let me quote from scalar.
Not SVM.
Let's import as we see and now what I'm trying to do.
Let me select the full classifier object and just we are going to change it like a instead of random
forests.
Let us use s.E.C.
And you just cannot supply this argument.
So if you see the documentation of this as we see.
It has created a full documentation.
So with that, we are going to supply the core argument.
Let's say the see arguing, let's just make it Hendrickx.
And Gamma, so gamma will be let's just make it part of.
And instead of classify it, I'm just going to use like a SVM object.
And let me execute.
All right.
So there is some typo here he's equal to.
I guess sees an unexpected argument that that is a capital is not a small city.
Let me get any.
All right.
Now, let just trained is they don't know it pessimism.
Martin.
So for that, we are going to use this ship X and the school plane.
And why this plane?
And let me then you.
All right, so our model got creative next days.
We need to predict this thing.
So I'm just going to select this one.
And instead of classify it, let's put it like a SVM.
So we got the result of a prediction.
Let's try to find now accuracy score and we will be supplying the scope test.
And let us see whether it got improved or not.
It will be my prediction.
All right.
So we got almost similar accuracy.
Ninety four point forty three in our earlier case, it was a ninety four point four.
It's a little less than random forest.
Accuracy, we got better somewhat similar accuracy we have achieved.
Let's try to find a confusing matrix for that.
All right.
Almost the same leg earlier.
We have a twenty five records.
Got it wrong.
But you know, it is it was 24 records.
Whereas the remaining all records completely predicate.
Right.
You can even do seem like a classification report also.
So let me pass here.
Whiteface I and this quote, Brad.
And it will give a detailed report.
So we this mistake.
Let's wrap it around the brain function.
All right.
So you can see that's calculated, this precision recall effort, schools, support for both of these
getting high.
Right.
So nothing much improvement, almost similar accuracy we got with this SVM model also.
But now we have two models.
Let's say it will be a classified model that is nothing but other random forest model.
And the other one is a has to be a model.
What we'll do next next year, you will apply instead of this testing that I say will apply, our own
created some spam and a harm message and we'll try to find whether it SVM our random forest.
Will you correct this or not?
So see you next video.
I'll let handcrafted tasting gas.
We will apply on top of.