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Hi! you probably learned a bit about flowers if you did the activity

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associated with the last lesson.

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Now, we're going to actually build
a classifier: Lesson 1.4

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Building a classifier.

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We're going to use a

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system called J48—I'll
tell you why it's called J48 in a minute—

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to analyze the glass dataset.

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That we looked at in the last lesson.

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I've got the glass dataset open here.

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I going to go to the Classify panel.

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I choose a classifier here.

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There are different kinds of
classifiers. Weka has

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bayes classifiers, functions classifiers,
lazy classifiers, meta classifiers, and so on.

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We're going to use a tree classifier. J48 is
a tree classifier. I'm going to open trees and click

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J48.

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Here is the J48 classifier.

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Let's run it. If we just press start,
we've got the dataset, we've got the classifier,

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and lo and behold,

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it's done it.

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It's a bit of an anticlimax, really.

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Weka makes things very easy

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for you to do.

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The problem is understanding what
it is that you have done.

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Let's take a look.

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Here is some information
about the datasets,

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glass dataset. The number of
instances and attributes.

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Then it's printed out a
representation of a tree here.

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We'll look at these trees later on,

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but just note that this tree has

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30 leaves and 59 nodes altogether.

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The overall accuracy

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is 66.8%.

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So, it's done pretty well.

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Down at the bottom,

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we've got a confusion matrix.
Remember there were about seven different

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kinds of glass.

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This is

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building windows made of float glass.

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You can see that 50 of these
have been classified as 'a', which is

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correctly classified.

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15 of them have been classified as 'b',

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which is building windows non-float glass,
so those are errors,

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and 3 have been classified as 'c',

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and so on.

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This is a confusion matrix.

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Most of the weight is down the
main diagonal, which

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we like to see because that
indicates correct classifications.

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Everything off the main diagonal

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indicates a misclassification.

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That's the confusion matrix.

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Let's investigate this a bit further.

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We're going to open a configuration
panel for J48.

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Remember I chose it

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by clicking the Choose button.

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Now, if I click it here,

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I get a configuration panel.

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I clicked J48 in this menu,
and I get a configuration panel, which

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gives a bunch of parameters.

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I'm not going to

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really talk about these parameters.
Let's just look at one of them, the unpruned

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parameter, which by default is false.

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What we've just done is to build a

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pruned tree, because unpruned is False.

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We can change this to

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make it true and build an unpruned tree.

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We've changed the configuration.

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We can run it again.

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It just ran again, and now we have

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a potentially different result.

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Let's just have a look. We have
67% correct classification.

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What did we have before?

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These are the runs.
This is the previous run,

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and there we had

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66.8%.

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Now, in this run that we've just done with

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the unpruned tree, we've got 67% accuracy,

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and the tree is the same size.

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That's one option.

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I'm just going to look at another option,
and then we'll look at some trees.

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I'm going to click the
configuration panel again,

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and I'm going to change

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the minNumObj parameter.

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What is that?

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That is the minimum number of
instances per leaf.

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I'm going to change that from 2

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up to 15

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to have larger leaves.

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These are the leaves of the tree here,

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and these numbers in
brackets are the number of

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instances that get to the leaf. When
there are two numbers, this means that one

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incorrectly classified instance
got to this leaf and five correctly

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classified instances got there.

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You can see that all of

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these leaves are pretty small,

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with sometimes just two or three

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or here is one with 31

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instances. We've constrained now this number,

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the tree is going to be generated,
and this number is always going to be

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15 or more. Let's run it again.

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Now we've got

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a worse result, 61%
correct classification, but a much

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smaller tree,

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with only eight leaves.

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Now, we can visualize this tree.

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If I right click

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on the line—these are the lines that describe
each of the runs that we've done, and this

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is the third run—if I right
click on that, I get a little menu,

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and I can visualize the tree.

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There it is. If I right click on empty
space, I can fit this to the screen.

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This is the decision tree.
This says first look at the

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Barium (Ba) content.

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If it's large, then it must be headlamps.

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If it's small, then Magnesium (Mg).

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If that's small, then let's look at potassium (K),
and if that's small, then we've got tableware.

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That sounds like a pretty good thing to me;
I don't want too much potassium in my tableware.

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This is a visualization of the tree

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and it's the same tree that you
can see by looking here.

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This is a different representation
of the same tree.

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I'll just show you one more
thing about this configuration panel,

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the More button. This
gives you more information

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about the classifier,

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about J48.

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It's always useful to look at that to
see where these classifiers have come from.

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In this case,

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let me explain why it's called
J48. It's based on a famous

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system that's called C4.5,
which was described in a book.

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The book is referenced here.

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In fact, I think I've got

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on my shelf here. This book here,

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"C4.5: Programs for Machine Learning"
by an Australian

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computer scientist called Ross Quinlan.

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He started out with a system called ID3—

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I think that might have
been in his PhD thesis—

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and then C4.5 became quite famous.
This kind of morphed through various

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versions into C4.5.

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It became famous; the book came out,
and so on. He continued to work on this system.

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It went up to C4.8,

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and then he went commercial. Up until
then, these were all open source

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systems.

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When we built Weka,

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we took the latest version

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of C4.5,

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which was C4.8,

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and we rewrote it. Weka's written
in Java, so we called it J48.

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Maybe it's not a

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very good name,

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but that's the name that stuck.

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There's a little bit of history for you.

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We've talked about classifiers in Weka.

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I've shown you where you find the
classifiers. We classified the glass

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dataset. We looked at how to interpret
the output from J48, in

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particular the confusion matrix.
We looked at the configuration panel for J48.

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We looked at a couple of options: pruned
versus unpruned trees and the option to

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avoid small leaves.

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I told you how

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J48 really corresponds to the
machine learning system that

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most people know as C4.5.
C4.5 and C4.8 were really pretty similar,

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so we just talk

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about J48 as if it's synonymous with C4.5.

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You can read about this in the book—

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Section 11.1 about Building a
decision tree and Examining the output.

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Now, off you go, and do the
activity associated with this lesson.

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See you again soon!