this episode of scishow is brought to
you by IBM Z learn more about the
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IBM dot biz / mainframe it's no secret
that machine learning is all the rage
it's the idea that we can use data to
train a computer to perform a task
without having to specifically program
in all the steps and it's a large
contributor to artificial intelligence
many of the flashiest applications of AI
like voice assistants or self-driving
cars are famous for using neural
networks these are powerful computer
programs designed to loosely mimic the
structure of the human brain but they're
not the only important part of machine
learning a class of algorithms called
decision trees have also quietly lurked
behind the scenes helping scientists and
companies sort through data for decades
and helping you in ways you might not
expect like neural networks decision
trees also emulate humans but instead of
copying the layout of our brain they
mimic the way we reason they classify
items or situations by checking how
their attributes compared to past
examples kind of like playing a guessing
game for instance if you are trying to
guess what kind of vehicle I'm thinking
of your first question if you're any
good at this would probably be something
that would eliminate whole categories of
answers before moving on to more
specific details so if you asked does it
have wheels and I said yes you might
follow up with how many and if I said to
the question well how fast does it go
would help you pick between like a bike
and a motorcycle decision trees work the
same way what makes them so flexible is
the wide variety of data they can handle
your three questions for example
included three kinds of data does it
have wheels was checking categorical
data not something represented by a
number vehicles can only have a whole
number of wheels so that question was
asking about discrete data while top
speed can be any number and is therefore
continuous data in day-to-day life we
deal with that variety of kinds of
information really easy
and decision trees are designed to do
the same thing they can also efficiently
handle huge amounts of information which
makes them useful in complex situations
one not so surprising application of
decision trees you probably run into all
the time is the recommendation systems
for things like streaming services and
restaurant booking apps when picking a
place to eat you might ask a friend how
much they want to spend or what sort of
food they're craving the exact style of
questioning decision trees work with and
their data flexibility means that the
system can take those answers and
combine them with other stuff the app
knows about you like the fact that you
invariably choose trash over something
that's probably going to be good for you
sorry speaking from personal experience
here recommender systems also have to
run a lot just think about how often you
fiddle with the little filters while
searching the like limit the travel
distance or max price because decision
trees rely on such simple logic they can
run faster than other machine learning
algorithms with similar capabilities of
course they are not perfect as the
number of choices grows the decision
tree can get bogged down a fact that we
humans know all too well when scrolling
endlessly for just the right movie to go
with that take out but programmers can
build in flexibility like returning a
list of recommendations instead of a
single one when the trees get too
complex that way they can work fast
enough to keep us happy at some point in
your life you've probably also had the
experience of getting your credit card
mysteriously declined while trying to
buy something mundane the bank was
likely trying to stop what they thought
was fraud but if it was really you it
probably like really slowed down your
day that's the case eighty percent of
the time and it stinks for the buyer and
the bank you can't buy the thing you
need or at least not with the card you
wanted to use and your bank first of all
misses out on making some money from
your transaction and second you might
even get annoyed and stop using that
card a situation one 2015 study
estimated could cost banks a hundred
billion dollars a year part of the
problem is that in the past fraud
detection has relied on a set
one-size-fits-all rules like I'm going
to make up a random example they might
decide that if you make a purchase in
two different states within an hour
something's probably wrong but maybe you
were on a road trip or you just live
near the border this very broad approach
is kind of silly especially since your
bank knows an extraordinary amount about
you from how often you shop online to
what stores you frequent and when you go
so in a 2018 study a group of MIT
researchers took more than 200 kinds of
data collected by credit card companies
and created a decision tree to predict
fraud the algorithm turned all that data
into tailored predictors like how often
you travel so instead of freaking out
the software would just realize hey this
behavior is actually pretty normal for
this person when tested on 1.8 million
new transactions the decision tree
reduced the error rate of fraud
prediction by 54% and as these
techniques are integrated on a wider
scale that will mean less fraud for
banks to deal with and fewer mysterious
problems for the rest of us whether it's
finding the right tie place or
protecting your private information a
decision tree is a great option for
getting the job done they reason like we
do and they can do it fast like think
about how many movies are on Netflix but
there's always a recommendation just for
you and yet they are still simple enough
to understand for us mere human beings
thanks for watching this episode of
scishow which was brought to you by IBM
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