- [Narrator] We are about to explore
global explanation techniques.
And we're going to start with Partial Dependence Plots
which graph the effect of a feature on the outcome.
Here, we've zoomed in on one variable
and a credit example
that we'll be using throughout our discussion
of global and local explanations.
The Y axis is risk and the X axis is times in debt
for one to two months.
How long someone is late and how often they are late
are strongly related to credit risk.
But after a value of four on X axis
the relationship seems to plateau.
Let's go into KNIME and do this example step by step.
First, I want to just direct you
to some information about this data set.
The data set is called, Give Me Some Credit
and it's found on the Kaggle website.
It's also referenced on the KNIME website
and I'll have more to say about that later
for now I'm going to suggest for at least this first video
and global explanations that you just watch get oriented
and then you can find the necessary workflows
and data sets and try it on your own.
Okay, here we are in KNIME.
This is a workflow which will be provided
in the course resources
named after the chapter 05 01 XAI PDP.
There is a similar workflow
available in the KNIME resources.
You can actually, we find it on the example server.
And if you scroll down to analytics
and then scroll down to machine learning interpretability
there actually is a sample workflow
on Partial Dependence Plots.
I have modified it so that we'll use the same data set
credit score throughout.
So you're looking at the modified version
and we read in the scoring data.
Then we partition.
Then we're using XG boost
which clearly is an example of a black box technique.
There's also a necessary pre-processing step.
Let's go in here for just a moment.
What I'm doing is I'm taking just a fraction
of the variables, some of the stronger relationships there.
And if you want to know a little bit more about this node
you can request the help for this node.
It's a necessary step, but from a theory standpoint
it's not the most important step.
So let's go right to the Partial Dependence Plot itself.
I'm going to click on cancel.
Here again the variables that I've chosen
are credit worthiness worthy and risky,
and three variables from the set of variables
to make this run a bit faster.
It's times in debt for one to two months
times in debt for three months or more
times in debt for two to three months.
Now let's execute this.
So I'm going to choose the interactive view maximize.
Okay, great.
So we can see our Partial Dependence Plot
and our X axis is times in debt for one to two months.
And our Y axis is risky.
So what we see
is that we have a risk between 0.05 and point 0.10
on the Y axis at no times in debt, but that rapidly climbs
and as it approaches around four or five, it then plateaus.
So we're getting a very visual look at the relationship
between this predictor and risky credit behavior.
Let's go on up to the upper right hand corner
the settings button, and here I can choose
from my list of features and the prediction.
Now, remember I've only sent it a subset of the predictions
but I'm not going to go to three months or more.
Give it a moment.
And we see a modified picture, don't we?
Because of course the X and Y axis haven't changed
but the graphic has changed.
Now we also see that rapid escalation pattern
but it starts to plateau more like three.
And the amount of risk is substantially higher
because this is now times in debt
for three months or more 90 days, not that smaller amount.
So there you have it.
That is a Partial Dependence Plot.
And once you look at your features
one at a time against your target variable.