- [Instructor] I'm a phycology professor
and if there's one thing I can tell you for sure,
it's that people are hard to understand
and that's why after more than two decades
I'm still working at it.
But you don't have to understand people completely
and be able to predict their behavior perfectly
in order to accomplish something worthwhile.
Really all you need is more insight
and better predictions than you might of had otherwise
and this is where we come into the field
of predictive analytics.
But first, I want to mention that there are two different
meanings to the word predict and predictive analytics.
The first is to use the standard one
that is to look into the future,
say what's going to happen at some later point in time.
So we're talking about future events
but also in predictive analytics a very common use
is to talk about alternative events.
So, maybe not in the future but what would happen
if an algorithm were to classify photos?
Could it do it in the same way that a human would?
Could the algorithm predict what a person would do
in the same situation?
And you'll find that some of the algorithms
are used more frequently for the
looking into the future,
whereas others are looked more often for the
alternative what would a human do in this situation?
Now in terms of predicting values
you might try to predict future
quantitative or categorical outcomes.
So, you might want to predict something like
the value of future purchases by a particular client
or the likelihood of remission of a disease in a patient.
In these cases, regression or linear regression
is a flexible and easy to interpret approach.
Here you see a picture of a scatter plot.
We've got an x-axis on the bottom,
a y-axis at the side and dots to indicate the scores,
and that line is the regression line.
That allows us to predict values, now
regressions a tremendously flexible approach.
There's about a million variations on it
that can adapt to the different needs of your data.
You might get an equation that looked
a little bit like this, that says
we're trying to predict the y outcome for individual i
and then we have an intercept and then we have
several variables given by x
and we multiply all of them by
their various coefficients
and that gives us a predicted score.
Now, more often what's going to happen is
you would actually run the computer
and it would simply tell you
what the predictive scores were for each person
but this is the, kind of the
calculating that's going into it.
You may also be interested in classifying cases
and for instance, you're looking at photos
and you want to see what group
would a human rater put the case in?
So, if a person were looking at this photo
what would they say is in it?
And there are different method there.
There's K-means clustering where K is the
number of groups that you want to create.
There's also K-nearest neighbors
where you put a photo into a category
based on what the other photos
that are similar to it are in.
There are decision trees and
in their plural the random forests.
And there are neural networks in many variations
and especially deep learning neural networks.
And all of those allow you to estimate
how a human would classify this if they
had enough time to go through all of the data
or at times to predict what would happen in the future.
But again remember people are complex
and while you may not be able to
predict or classify things with perfect accuracy
that's usually not the goal, I mean
the rate of agreement between
two trained human radars is never 100%,
just get two doctors to evaluate the same patient.
So what your looking for is incremental improvement
step-by-step or the ability to get a better picture
and a better idea than you had before
and to then do something useful with that insight.
That is the goal, that's the reason for being
of predictive analytics in the big data world.