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All right.

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And that's all you will see
that it will work perfectly well.

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So I'll show you right now

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let's select what's above,
which is all the pre-processing steps.

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So command and control
plus end to execute.

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All right. Pre-processing. Well done.

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We are good to go.

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And now we can create our classifier.

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So yes let's do this.

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If your 1071 package is not selected
you need to select that as well.

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So command and control
plus enter to execute.

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And here is our classifier.

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Awesome.

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So as you can see it works perfectly well.

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The classifier is well created
using only those two informations.

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And when you think about it

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well this is all
we need to train a classifier

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because we need the information
of the independent variables

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and the information

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of the dependent variable,
so that it can learn the correlations

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between the independent variables
and the dependent variable.

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All right. So that makes sense.

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And now we can create our vector
of prediction.

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Why pred using the predict function
on our classifier and on our new data

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which is the test set.

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So here we go.

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White bread is created.

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Let's have a quick look. Why pred.

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So for the first time we are obtaining
something here.

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Remember before when we entered white
bread in the console,

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we had all the predictions
listed in the console.

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But here we have this factor zero.

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Here.

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So that means that white bread
is a vector of vectors but with no vector.

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So it's basically an empty vector.

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And that is because the naive Bayes
function of the 1871 library

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didn't recognize our dependent variable
vector purchased

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as a categorical variable
with zero and one factors.

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So far, the libraries and functions
we've been using

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recognized
the dependent variable as factors.

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So we didn't have any issues
with our predictions,

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and we didn't have to encode the dependent
variable purchased as factors.

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But here with Naive
Bayes it's not the case.

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It doesn't recognize the dependent
variable purchased as vectors.

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So we need to encode it as factors.

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Because as you can see,
if we try to compute the confusion

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matrix below,
we will get this following error message.

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All arguments must have the same length.

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Well yes indeed we have a problem because
the two arguments here are to set three.

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That is the third column purchased
of the test set, and then white bread.

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So since the set three has length
100 and white bread has length

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zero, because it's an empty vector
with no vectors,

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then obviously we can not compute
any confusion matrix.

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So what we need to do
is to jump back up to the pre-processing.

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First step to encode are dependent
variable purchased as vectors.

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Then the naive base function
will recognize the dependent variable

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as vectors, and will perfectly be able
to create a classifier

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that will allow the predict function
to return the expected vector

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of predictions of y print.

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So it's great that you see this error,
because this is a classic error in machine

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learning in R, and that way from now on,
you will make this error in the future.

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Or if you make it,
you'll know how to fix it.

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So let's do it right now.

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Let's add a new code section here.

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And let's call it and coding

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the target feature

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as vector.

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All right.

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And now let's factorize R column purchased
okay.

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Let's do this.

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So we will take it from start.

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That means that we will take the purchased

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dependent variable column of the data set.

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And then we will recompute all of these
to set

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this purchase column everywhere
for all of set.

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That is a training set.
And the test set okay.

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So we're going to take our last column
from our data set.

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So we're going to type data
set dollar purchased

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write equals

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factor and in parenthesis data set.

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00:04:01,666 --> 00:04:04,966
And then again
we're taking the purchased column

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that is the dependent
variable column of our data set.

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00:04:07,033 --> 00:04:11,400
So again we are taking the dollar sign
here and purchased to take this column.

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And then here

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we will
just specify the levels or levels are

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C zero and one.

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All right.

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And that's it.

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That's how you encode your dependent
variable column purchased into factors.

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All right.
So we're going to start from scratch now.

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So to do this we can clear everything.

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So that's what
we're going to do to everything here.

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So we're cleaning the data 
clearing the script here.

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So I'm pressing control L to clear.

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And that's.