All right.
So that's for the train set.
And now let's come in quickly on the test set.
All right. This is already executed. Perfect.
So here are the results of Naive Bayes on the test set.
And actually we realized that here we were pretty unlucky.
You know, naive Bayes was pretty unlucky
because the three predictions that it missed, you know,
this red customer that falls incorrectly in the green region.
And these two green customers that fall incorrectly in the red region.
Well, Naive Bayes was pretty unlucky on this one
because, you know, they're almost on the prediction boundary
and it is what it missed by very closely to beat the record.
And same for this one actually.
You know, this one was very shortly missed. Right.
Because it is an incorrect prediction of a customer who in reality
didn't buy the SV because it's red, but was predicted to by DSV
because it falls in the green region.
So pretty unlucky.
Which explains the accuracy of 90%.
I think that if we tune a bit Naive Bayes, we could beat the record.
But once again, this will be important and now the next step will be to buy
the last two classification models,
which belong to a branch of machine learning called and Simple models.
I'm talking, of course, about decision tree
classification and random forest classification.
And actually, with these tools and especially random forest,
we might have a chance to beat that record accuracy.
You know, we might beat 93%.
So that's what on the menu for this end of part three.
So I can't wait to see you
in the next part to build first the decision tree classification model
and then finally the random forest classification.
And until then, enjoy machine learning.