- Up until now you've seen how supervised learning
can be used for classification.
You can use your neural network to classify music
into different categories.
They use human-created categories to classify
your music into jazz, blues, or even country.
Remember that this is different
from unsupervised learning.
In unsupervised learning,
you have the artificial neural network
create its own categories.
These machines create categories that may or may not
be the same as their human counterparts.
The marching band could put country music and blues
into the same category.
Classification isn't the only form
of supervised machine learning.
You can also have your artificial neural network
use something called regression.
Regression analysis is when you look at the relationship
between a dependent variable
and many other independent variables.
When I was younger,
I used to have an apartment that overlooked Lake Michigan.
In the summer,
I could use regression analysis to see how many people
would be sitting on the beach.
On the hot days,
the number of people on the beach would increase.
In fact, the hotter it got,
the more people you would see trying to cool off
in the large blue lake.
In this case, the number of people on the beach was
the dependent variable.
It would be higher or lower
based on several independent variables.
These independent variables could be
the outside temperature,
rain or even the day of the week.
After a few weeks of observing the behavior,
I could accurately predict how many people would be
on the beach that day.
In fact, that's why a lot of organizations
like regression analysis.
If you understand the relationship
between the dependent and independent variables,
you can often do a pretty good job of predicting
people's behavior.
I once worked for a credit card processing organization
that was trying to look for warning signs
for when their customer will have trouble paying their bill.
They used a regression in their artificial neural network
to try to find relationships between different variables.
The dependent variable was the likelihood of them
not paying their bill.
The independent variables was the different items
or amounts they charged on their credit card
before they were billed for their purchase.
What they found is that many costumers start to put
essentials on their credit card
just before they have trouble paying their bill.
So a customer who only uses their credit card
for large purchases,
such as a television or a computer,
would suddenly start putting essential purchases on them,
like groceries and gasoline.
Their artificial neural network found that
there was a relationship between these different variables.
If you increase the purchase of groceries,
gasoline, and utility bills,
then the likelihood of late payments started to increase.
Even though the information you get from regression analysis
is much different,
the way you use your artificial neural network
is pretty much the same.
It still requires massive amounts of data to find patterns.
Then the network looks for imperceptible patterns
in large data sets.
In one, you're using the data to classify.
And in the other,
you're using the data to find relationships.
When you're starting out your own AI project,
it's important to determine whether you want to classify
or use regression analysis.
Think of it as a difference between sorting and connecting.
If your project is looking for patterns to sort your data
into different categories,
then you're going to want to stick with classification.
This is when your artificial neural network
can group together pictures of cats
or identify different types of music.
If your project is looking for many different patterns
to connect,
many different variables,
then you're going to want to try regression.
It's here where your artificial neural network
processes what happens between the dependent
and several independent variables.
Then it identifies patterns,
so you can predict what happens.
One thing to keep in mind is that your artificial neural
network will only show you the patterns.
It doesn't necessarily provide the answers.
The credit card company also found that people
who had a lot of purchases under $5
were likely to have trouble paying their bill.
The network didn't really give anyone any idea
for why this occurred.
It just pointed out that it was happening.
After they found the pattern,
it was up to human beings to try to sort out
why this happens.
Perhaps at some point,
these artificial neural networks will be able to create
their own theories for why these patterns exist.
For now, they pretty much rely on their human counterparts
to find meaning in these connections.