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Hi! Welcome back for another
five minutes in New Zealand

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with Data Mining with Weka.

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This is Lesson 1.3, and we're going
to look at exploring datasets

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in this lesson.

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We looked at this data file in the 
last lesson. It's the

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weather data

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toy dataset, of course. It has
fourteen days, or

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instances, and each instance, 
each day, is described by

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five attributes,

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four to do with the weather, and

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the last attribute,

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which we called the class value,

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the thing that we're trying to 
predict, whether or not to play this

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unspecified game.

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This is called a classification problem.

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We're trying to predict the class value.

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Let's open up Weka.

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It's here on my desktop.

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I'm going to go into the Explorer.

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We always use the Explorer.

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I'm going to open the file.

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I put the datasets in My Documents folder, 
 so I can see them here.

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Just open

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the Weka datasets and 
the nominal weather data.

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There's the weather data in Weka.

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As we saw last time,

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you can see the size of the dataset, 
the number of instances—fourteen—

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you can see the attributes,

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you can click any of these attributes

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and get the values for those attributes

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up here in this panel.

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You also get at the bottom
a histogram of the attribute values

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with respect to the different
class values. The different class

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values are

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blue for yes, play and

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red for no, don't play.

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By default,

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the last attribute in Weka is always the class value.

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You can change this if you like. If you
change it here you can decide to

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predict a different one other than the last
attribute.

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That's the weather dataset, and
we've already explored that.

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As I said, it's a classification problem,
sometimes called a supervised learning

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problem. Supervised

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because you get to know the

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class values of the training instances.

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We take as inputted data 
set as classified examples, 

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these examples are independent 
examples with a class value attached.

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The idea is to produce automatically 

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some kind of model 

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that can classify new examples. 

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That's the classification problem. 

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Here is what the examples 
look like. This is an instance, with 

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the different attribute values 

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a fixed set of features,

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 and then we add to that 

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the class to get the classified example. 

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That's what we have to 
have in our training dataset. 

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These attributes or features 
can be discrete or continuous. 

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What we 

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looked at in the weather data were 

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discrete, or we call them nominal, 

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attribute values where they 
belong to a certain fixed set, 

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or they can be numeric 

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or continuous values. 

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Also, the class can be discrete or 
continuous. We're looking at a discrete class, 

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yes or no, in the case of the weather 
data. Another kind of machine 

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learning problem would involve 

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continuous classes, where 
you're trying to predict a number. 

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That's called a regression problem

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 in the trade.

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I'm going to have a look at a similar 

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dataset to the weather dataset. 

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The numeric weather

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dataset. 

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Let me just open that in Weka, 

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weather.numeric.arff. 

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Here it is. It's very similar, 

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almost identical in fact, 

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for 14 instances, 5 attributes, the same attributes. 

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Maybe I should just look at this dataset 

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in the edit panel. 

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You can see here that two of the 
attributes—temperature and humidity—

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are numeric attributes, whereas 
previously they were nominal

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attributes. So here there are numbers.

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What we see when we look at 
the attributes values for outlook, just as 

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before, we have 

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sunny, overcast and rainy. 

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For temperature, though, 
we can't enumerate the values, 

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there are too many numbers to enumerate. 

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We have the minimum and   maximum 
value, mean, and standard deviation. 

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That's what Weka gives you 

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for the numeric values.

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I'm going to look at a different dataset. 

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I'm going to look at the glass dataset, 
which is a rather more extensive dataset. 

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It's a real world dataset, 

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not a terribly big one. 

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Let's open it. 

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Here we've got 214 instances 

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and 10 attributes. 

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Here are the 10 attributes, 
it's not clear what they are. 

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Let's look at the class, 

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by default the last 

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attribute shown. 

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There are seven values for the class, 
and the labels of these values give 

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you some indication of what 
this dataset is about. 

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We have headlamps, tableware, and containers. 

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Then we have building and vehicle windows, 

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both float and non-float. 

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You may not know this, but there are 

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different ways of making glass, and 

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the floating process is a way of making glass. 

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These are seven different kinds of glass. 

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What are the attribute values? 

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I don't know what you remember about physics, 

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and I guess it doesn't 
matter if you don't remember. 

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RI stands for the refractive index. 

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It's always a good idea to check for 
reasonableness when you're looking at 

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datasets. It's really important to 

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get down and dirty with your data. 

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Here we're looking at the values of the 
refractive index—a minimum of 1.511,

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a maximum of 1.534. 

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It's good to think about whether these are 

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reasonable values for refractive index. If you 
go to the web and have a look around, 

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you'll find that these are 

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good values for 

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the refractive index.

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Na. 

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If you did chemistry, you'll recognize Na as sodium. 

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Here, it looks like these are percentages, 

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the different percentages of sodium. 

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Magnesium, Mg, 

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and so on. We would expect Silicon (Si), 

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to make up the majority of glass. 
It varies between 69.81% 

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and 75.41%.  

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These are percentages of 
different elements in the glass. 

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We can confirm our guesses 
here by looking at the data file itself. 

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Let me just find the glass data. 

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It's in Weka datasets, 

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and it's glass.arff. 

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This is the ARFF 

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file format. 

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It starts with a bunch of comments about 

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the glass database. These lines beginning 
with percentage signs (%) are comments. 

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You can read about this. 
We don't have time to read it now.

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You can see about the 
attributes and it does say that 

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the attributes are 

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refractive index, sodium, magnesium, and so on. 

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And the type of glass, just like I said, is about 

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windows, containers, and tableware, and so on.

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We can get down to the end of the comments, 

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and here we have stuff for Weka. This is 
the ARFF format. The relation has a 

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name, 

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you'll see it printed in 
the interface when you look. 

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The attributes are defined, 
they are real valued attributes,

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 numeric attributes. 

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The type 

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attribute is nominal, and 
the different values of type are

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 enumerated here in quotes. 

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That defines the relation 
and the attributes. Then we have an

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 '@data' line, and following that in the 
ARFF format, are simply the instances, 

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one after the other, with the attribute 
values all on one line, ending with 

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class by default. This is the 

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class value for the first instance. 

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I think there are 214 

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instances here. 

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There's the last one. 

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That's the ARFF format. It is a very simple, 

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textual file format. 

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Now we've confirmed our guesses 
about these numbers being percentages 

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and different elements. 

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We can think about 

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this some more. It's important 
then, that these numbers are

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reasonable. If they went negative, for example, 

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that would indicate some kind of corrupted 
value. You can't have a negative 

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percentage. 

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We're expected silicon to 
be the majority component; 

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we're expecting the refractive index to be 
in this kind of range. It's always a good 

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idea when you get a dataset to just 

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click around in the Weka interface 

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and make sure things look real. 
Rather small amounts 

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of aluminum in glass. I guess that's not surprising; 

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I don't know very much about glass myself. 

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We're just kind of checking for reasonableness here—

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a very good thing to do.

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That's it then. 

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In this lesson, we've 
looked at the classification problem. 

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We've looked at the nominal weather 
data and the numeric weather data. 

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We've talked about 
nominal versus numeric attributes, 

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and we've 

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talked about the ARFF file format. 

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We've looked at the glass.arff 

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dataset, 

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and I've talked about sanity checking 
of attributes, and the importance of 

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getting down and dirty with your data. 

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If you'd like some further background 
on this, you can read Section 11.1 

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of the text and read about 
Preparing the data and Loading the data 

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into the Explorer.

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Whether or not you do that, 

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please go and look at the activity 
associated with this lesson.

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We'll see you soon. Bye!