It beats by very little.
You know, the accuracy of the kernel SVM.
The kernel SVM did eight incorrect predictions.
Five plus three.
And the decision tree classification did four plus three seven incorrect
predictions, resulting in having an accuracy almost equal to 96%.
Well, that's really, really good.
You know, this is the first time actually that I'm doing this.
You know, trying this breast cancer data set with you, with all these models.
I tried it once with logistic regression in another course.
But this is the first time that I implement all these models
and deploy them to do a model selection process on this data set.
So, you know, that shows you how I'm confident about these code templates.
I never tried them before on this dataset.
And now I'm making this demo with you for the first time on this data set.
All right. So therefore the excitement.
And now final one I'm very curious to see if we can still beat it
with random forest classification.
So let's do this run all and final accuracy is whoa.
Oh. All right okay. So I'm very disappointing.
The random forest classification indeed really messed up with the teamwork.
And that's another exception to the rule because,
you know, usually teamwork is better than individual work.
But no, we had a very powerful decision tree classification model before
and it didn't need anybody else to be performant.
All right.
So that's very interesting.
Actually this is very surprising results.
But that's why it's really,
really important to try all your models and have these very efficient code
templates that allow you to do a quick and efficient
model selection process to quickly figure out the best model.
All right.
You have to understand that there is no rule of thumb.
And for some other data sets with other machine learning problems,
the best model will be another one of these five models.
All right.
So it was really important for me to show you this and to give you this okay.
So now there we go. My friends.
We are at the end of part three classification.
Big congratulations to you for, well, completing this part
three and making such a good progress on this course.
Now in the next part
we will start clustering, which will be our first unsupervised model.
I remind that the difference between supervised and unsupervised
is that with supervised learning, you know what to predict.
You know which dependent variable to predict.
And with unsupervised learning, you don't know what to predict,
and you will actually have to find some patterns in the data
to figure out something you could predict as a dependent variable,
but that you don't have a priori and that you create a posteriori.
All right, so no worries, we will see all this in the next part.
And until then, I think you deserve a good break now.
So get some good rest, relax a bit, and as soon as you're back to back
with some great energy, well, join us in the next part to tackle cluster
and until then, enjoy machine learning.