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Perfect.

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The next step is to, of course,
create an instance of this class,

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which will be an object representing
exactly that naive base model.

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And so there we go.

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We're going to call this
as usual classifier

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in order to be coherent
with the next sections

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00:00:16,500 --> 00:00:20,000
of this implementation, and mostly
so that we don't have to change anything.

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Right.

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Because then we call this

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00:00:21,933 --> 00:00:25,800
classifier variable to predict the results
and visualize the result.

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So there we go classifier here.

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And then and then we're going to call this
Gaussian NB class

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in order to create
indeed this Naive Bayes model.

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Okay. Perfect.

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And now you know how to finish this.

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We need to take our classifier again,
from which we're going to call the fit

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00:00:44,633 --> 00:00:49,166
method, which will train this classifier
on the training set

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composed of indeed x train and Y train.

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Right?

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I hope you did it even faster than me,

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because indeed,
this is exactly the same as before.

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And now you are also independent

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and know how to find the information
you need in the API.

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Okay, great. So once again there we go.

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00:01:09,533 --> 00:01:14,100
That implementation is over
and we ready to get the final result.

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And mostly we're ready to find out
if we can beat

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the record accuracy you know if 93%.

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So I can't wait to see.
So let's do it right now.

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Let's click
this folder button to upload a data set.

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Right.

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We have to do it in order to train.

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Indeed that Naive Bayes
model on the training set.

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Right now your notebook is connecting
to a runtime to enable file browsing.

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And once again, in this second
we should get the up button.

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There we go. So we're going to click it.

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And here we are in the kernel SVM folder.

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So let me show you the path once again
please.

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Fine your whole machinery.

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00:01:49,733 --> 00:01:54,000
It is that folder which you could download
in the previous tutorial if not already.

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And then inside we're going to go to port
three classification.

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Then section 18.

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We're making good progress here.

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00:02:00,600 --> 00:02:05,200
Naive Bayes
then Python and then social network

44
00:02:05,200 --> 00:02:08,633
ads dot csv okay we press okay.

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Here we have the data set. All good.

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And now now we can run everything
in order to get indeed our new result.

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00:02:16,800 --> 00:02:19,033
So let's do this run all.

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And now all the cells are running.

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00:02:21,166 --> 00:02:22,800
And especially this one. There we go.

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00:02:22,800 --> 00:02:26,066
We now have our Gaussian
Naive Bayes model.

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00:02:26,433 --> 00:02:30,100
And well let's see the results one by one
starting with this one.

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00:02:30,100 --> 00:02:32,633
So that's the prediction
of a single result.

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00:02:32,633 --> 00:02:34,266
You know that first customer of the test

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set of age
30 and estimated salary $87,000.

55
00:02:38,933 --> 00:02:41,966
And remember in the white
says the real outcome

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00:02:41,966 --> 00:02:45,000
was zero meaning
that this customer didn't buy the SUV.

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00:02:45,200 --> 00:02:48,466
And that's the prediction,
which is indeed the correct prediction.

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And then when predicting the test results,
well,

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00:02:51,766 --> 00:02:55,200
once again we see that
we have a lot of correct predictions.

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All this is correct.

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00:02:56,233 --> 00:02:57,300
All this is correct.

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00:02:57,300 --> 00:02:59,400
This is our first incorrect prediction.

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Another one here
and then another one here.

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00:03:02,866 --> 00:03:05,933
All correct. I'll correct
another one here.

65
00:03:06,300 --> 00:03:09,566
Oh I'm not sure
we're going to beat actually that accuracy

66
00:03:09,566 --> 00:03:14,333
we seem to have more than seven incorrect
predictions at first I'm not sure.

67
00:03:14,333 --> 00:03:15,600
But let's see, let's see.

68
00:03:15,600 --> 00:03:18,233
Well, that's exactly
what we're about to find out right now.

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So are you ready?

70
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The question is,
will we beat the accuracy of 93%?

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Which was the best accuracy resulting
from both Kilian and any kernel SVM.

72
00:03:28,200 --> 00:03:30,833
And so let's see what we get
with Naive Bayes.

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00:03:30,833 --> 00:03:33,833
And no,
unfortunately we don't beat the record.

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00:03:34,100 --> 00:03:38,633
Indeed, the accuracy
we get with that Naive Bayes model is 90%,

75
00:03:38,633 --> 00:03:42,666
which beats indeed logistic regression,
but does equally the same

76
00:03:42,666 --> 00:03:46,000
as the classic SVM model
with a linear kernel.

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00:03:46,666 --> 00:03:50,566
All right, but still, I think we will get
nice visualization results.

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00:03:50,633 --> 00:03:53,733
That's the code cell where we visualize
the training set results.

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00:03:53,733 --> 00:03:56,266
And well,
this time we got the results pretty fast.

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00:03:56,266 --> 00:03:59,100
You can see that
the cell is already executed.

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00:03:59,100 --> 00:04:02,700
So let's see
I can show you that indeed the Naive Bayes

82
00:04:02,700 --> 00:04:04,833
curve is pretty nice right?

83
00:04:04,833 --> 00:04:07,800
It is a nice smooth curve right?

84
00:04:07,800 --> 00:04:10,533
That catches well indeed these

85
00:04:10,533 --> 00:04:13,500
green customers
here, you know, the ones who in reality.

86
00:04:13,500 --> 00:04:18,400
But the SUV in the right green region
but unfortunately you know it's separated

87
00:04:18,400 --> 00:04:19,066
the two classes.

88
00:04:19,066 --> 00:04:19,233
You know

89
00:04:19,233 --> 00:04:23,100
with these two prediction regions
a bit large, you know, not very precisely.

90
00:04:23,100 --> 00:04:26,533
And that's why we don't get an accuracy
that is higher than 93%.

91
00:04:26,766 --> 00:04:30,066
But still, you know, we made a progress
with respect to logistic regression

92
00:04:30,066 --> 00:04:33,066
because indeed
remember that for logistic regression

93
00:04:33,200 --> 00:04:36,700
these green customers here could not be
well classified.

94
00:04:36,766 --> 00:04:38,300
Right. Because of the straight line.

95
00:04:38,300 --> 00:04:40,366
They fall in the red region.

96
00:04:40,366 --> 00:04:44,266
And here in our Naive Bayes
implementation, well these green customers

97
00:04:44,266 --> 00:04:47,200
fall in the right region.
So at least it corrected that.

98
00:04:47,200 --> 00:04:51,666
But since here there is kind of
a large margin, well these green customers

99
00:04:51,666 --> 00:04:55,633
which were correctly classified
with the kernel SVM,

100
00:04:55,633 --> 00:04:58,933
if you remember right,
these are the training set results.

101
00:04:59,300 --> 00:05:01,266
Right.
So I'm talking about these ones here.

102
00:05:01,266 --> 00:05:04,466
They are correctly classified
except these two and the third one.

103
00:05:04,466 --> 00:05:06,400
But clearly with Naive Bayes.

104
00:05:06,400 --> 00:05:09,000
Well they fall into the wrong region okay.

105
00:05:09,000 --> 00:05:13,566
But anyway, at least you see the
prediction curve of the Naive Bayes model.

106
00:05:13,566 --> 00:05:18,166
And mostly you see that Naive Bayes
model is clearly a nonlinear classifier.

107
00:05:18,400 --> 00:05:22,800
And, you know, in some other situations,
because the naive Bayes is causing less

108
00:05:22,800 --> 00:05:27,300
overfitting, well, in some situations
it will do better than your other models.

109
00:05:27,300 --> 00:05:30,033
That's why it's always very important
to try all of them.

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00:05:30,033 --> 00:05:33,333
And remember, at the end of this part,
I will actually deploy

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00:05:33,333 --> 00:05:37,100
all our models with new simplified code
templates for each model.

112
00:05:37,100 --> 00:05:39,000
You know, without all the prints
and everything,

113
00:05:39,000 --> 00:05:42,566
in order to deploy them in a flashlight
so that we can quickly figure out

114
00:05:42,733 --> 00:05:46,266
what is the best classification model
for any data set.

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00:05:46,266 --> 00:05:48,300
You know,
regardless of the number of features.