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Anomaly Detection vs. Supervised Learning
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From the course by Stanford University
Machine Learning
97121 ratings
From the lesson
Anomaly Detection
Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.
Meet the Instructors
Andrew NgCEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
In the last video we talked about the process of evaluating
an anomaly detection algorithm.
And there we started to use some label data with examples that we knew
were either anomalous or not anomalous with Y equals one, or Y equals 0.
And so, the question then arises of, and if we have the label data, that we have
some examples and know the anomalies, and some of them will not be anomalies.
Why don't we just use a supervisor on half of them?
So why don't we just use logistic regression, or
a neuro network to try to learn directly from our labeled data
to predict whether Y equals one or Y equals 0.
In this video, I'll try to share with you some of the thinking and some guidelines
for when you should probably use an anomaly detection algorithm, and
whether it might be more fruitful instead of using a supervisor in the algorithm.
This slide shows what are the settings under which you should maybe use
anomaly detection versus when supervised learning might be more fruitful.
If you have a problem with a very small number of positive examples, and
remember the examples of y equals one are the anomaly examples.
Then you might consider using an anomaly detection algorithm instead.
So, having 0 to 20, it may be up to 50 positive examples,
might be pretty typical.
And usually we have such a small positive, set of positive examples, we're going to
save the positive examples just for the cross validation set in the test set.
And in contrast, in a typical normal anomaly detection setting,
we will often have a relatively large number of negative examples
of the normal examples of normal aircraft engines.
And we can then use this very large number of negative examples With
which to fit the model p(x).
And so there's this idea that in many anomaly detection applications,
you have very few positive examples and lots of negative examples.
And when we're doing the process of estimating p(x),
affecting all those Gaussian parameters, we need only negative examples to do that.
So if you have a lot negative data, we can still fit p(x) pretty well.
In contrast, for supervised learning, more typically we would have a reasonably
large number of both positive and negative examples.
And so this is one way to look at your problem and
decide if you should use an anomaly detection algorithm or a supervised.
Here's another way that people often think about anomaly detection.
So for anomaly detection applications,
often there are very different types of anomalies.
So think about so many different ways for go wrong.
There are so many things that could go wrong that could the aircraft engine.
And so if that's the case, and if you have a pretty small set of positive examples,
then it can be hard for an algorithm, difficult for an algorithm
to learn from your small set of positive examples what the anomalies look like.
And in particular,
you know future anomalies may look nothing like the ones you've seen so far.
So maybe in your set of positive examples, maybe you've seen 5 or 10 or
20 different ways that an aircraft engine could go wrong.
But maybe tomorrow, you need to detect a totally new set,
a totally new type of anomaly.
A totally new way for
an aircraft engine to be broken, that you've just never seen before.
And if that's the case,
it might be more promising to just model the negative examples with this sort of
calcium model p of x instead of try to hard to model the positive examples.
Because tomorrow's anomaly may be nothing like the ones you've seen so far.
In contrast, in some other problems, you have enough positive examples for
an algorithm to get a sense of what the positive examples are like.
In particular, if you think that future positive examples are likely to be similar
to ones in the training set; then in that setting,
it might be more reasonable to have a supervisor in the algorithm that looks at
all of the positive examples, looks at all of the negative examples, and
uses that to try to distinguish between positives and negatives.
Hopefully, this gives you a sense of if you have a specific problem,
should you think about using an anomaly detection algorithm, or
a supervised learning algorithm.
And a key difference really is that in anomaly detection, often we
have such a small number of positive examples that it is not possible for
a learning algorithm to learn that much from the positive examples.
And so what we do instead is take a large set of negative examples and
have it just learn a lot, learn p(x) from just the negative examples.
Of the normal [INAUDIBLE] and
we've reserved the small number of positive examples for evaluating
our algorithms to use in the either the transvalidation set or the test set.
And just as a side comment about this many different types of easier.
In some earlier videos we talked about the email spam examples.
In those examples, there are actually many different types of spam email, right?
There's spam email that's trying to sell you things.
Spam email trying to steal your passwords, this is called phishing emails and
many different types of spam emails.
But for the spam problem we usually have enough examples of spam
email to see most of these different types of spam email
because we have a large set of examples of spam.
And that's why we usually think of spam as a supervised learning setting
even though there are many different types of.
If we look at some applications of anomaly detection versus supervised learning
we'll find fraud detection.
If you have many different types of ways for people to try to commit fraud and
a relatively small number of fraudulent users on your website,
then I use an anomaly detection algorithm.
I should say, if you have, if you're a very major online retailer and
if you actually have had a lot of people commit fraud on your website, so
you actually have a lot of examples of y=1, then sometimes
fraud detection could actually shift over to the supervised learning column.
But, if you haven't seen that many examples of users doing strange things
on your website, then more frequently fraud detection is actually treated as
an anomaly detection algorithm rather than a supervised learning algorithm.
Other examples, we've talked about manufacturing already.
Hopefully, you see more and more examples are not that many anomalies but
if again for some manufacturing processes,
if you manufacture in very large volumes and you see a lot of bad examples,
maybe manufacturing can shift to the supervised learning column as well.
But if you haven't seen that many bad examples of so to do the anomaly detection
monitoring machines in a data center [INAUDIBLE] similar source of apply.
Whereas, you must have classification,
weather prediction, and classifying cancers.
If you have equal numbers of positive and negative examples.
Your positive and your negative examples,
then we would tend to treat all of these as supervisor problems.
So hopefully, that gives you a sense of
one of the properties of a learning problem that would cause
you to treat it as an anomaly detection problem versus a supervisory problem.
And for many other problems that are faced by various technology companies and so
on, we actually are in the settings where we have very few or
sometimes zero positive training examples.
There's just so
many different types of anomalies that we've never seen them before.
And for those sorts of problems,
very often the algorithm that is used is an anomaly detection algorithm.
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