Gaussian NB Example - Intro to Machine Learning

• 0:00 - 0:04
Okay, so now what I've done is I've gone to the Gaussian Naive Bayes
• 0:04 - 0:05
documentation page.
• 0:05 - 0:08
sklearn.naive_bayes.GaussianNB.
• 0:08 - 0:11
This was that algorithm that I set out to find and
• 0:11 - 0:16
now that I've, now I've found the SK Learn documentation page.
• 0:16 - 0:18
So the first thing that I see right here, actually this is one of the things I
• 0:18 - 0:23
love about the SK Learn documentation, is it's full of examples.
• 0:23 - 0:26
When I was actually developing the code for this class, this was one of
• 0:26 - 0:29
the first things that I would always do is I would come find the example code
• 0:29 - 0:31
and I would try to just run in my Python interpreter,
• 0:31 - 0:33
see if I could get it working.
• 0:33 - 0:36
And almost invariably it works right out of the box.
• 0:36 - 0:38
So here's something that's just very simple.
• 0:38 - 0:40
There's only a few lines here that are really important.
• 0:40 - 0:41
So let me point them out to you and
• 0:41 - 0:46
then I'll show you the code I've actually written for the example we just saw,
• 0:46 - 0:48
and you'll start to recognize some of these lines.
• 0:48 - 0:49
But first let's introduce them.
• 0:49 - 0:52
So the first one that's really important is this one right here.
• 0:54 - 0:57
Above this it's just creating some, some training points that we can use,
• 0:57 - 0:58
it's not that important.
• 0:58 - 1:02
This is where the real meat starts, is with this import statement and if you've
• 1:02 - 1:05
programmed in Python before, you're well acquainted with import statements.
• 1:05 - 1:08
This is the way that you bring in external modules into the code that you're
• 1:08 - 1:12
writing so that you don't have to completely re-implement everything every time,
• 1:12 - 1:15
you can use code that someone else has already written.
• 1:15 - 1:19
So we say from sklearn.naive_bayes going to import GaussianNB.
• 1:19 - 1:20
Very good.
• 1:20 - 1:23
The next thing that we're going to do is we're going to use that to
• 1:23 - 1:24
create a classifier.
• 1:24 - 1:27
So classifier equals GaussianNB.
• 1:27 - 1:29
If you miss your import statement.
• 1:29 - 1:30
If you forget this line for
• 1:30 - 1:32
some reason, then this line is going to throw an error.
• 1:32 - 1:36
So if you end up seeing some kind of error that says that it doesn't recognize
• 1:36 - 1:38
this function.
• 1:38 - 1:40
It's probably a problem with your import statement.
• 1:40 - 1:43
So, okay, we've created our classifier.
• 1:43 - 1:45
So now the code is all sort of ready to go.
• 1:45 - 1:47
The next thing that we need to do is we need to fit it.
• 1:49 - 1:52
And we've been using the word train interchangeably with fit.
• 1:52 - 1:55
So this is where we actually give it the training data,
• 1:55 - 1:57
and it learns the patterns.
• 1:57 - 2:00
So we have the classifier that we just created.
• 2:00 - 2:04
We're calling the fit function on it, and then the two arguments that we pass to
• 2:04 - 2:10
fit are x, which in this case are the features and y which are the labels.
• 2:10 - 2:13
This is always going to be true in supervised classification.
• 2:13 - 2:15
Is that it's going to call this fit function and
• 2:15 - 2:16
then it's going to have the features.
• 2:16 - 2:17
And then the labels.
• 2:19 - 2:22
And then the last thing that we do is we ask the classifier that
• 2:22 - 2:24
we've just trained for some predictions.
• 2:24 - 2:25
So we give it a new point.
• 2:25 - 2:29
In this case the point is negative 0.8, negative 1.
• 2:29 - 2:33
And we ask for this what do you think the label is for this particular point?
• 2:33 - 2:35
What's the, what class does it belong to?
• 2:35 - 2:38
So in this particular case it says it belongs to class number one.
• 2:38 - 2:42
Or you could imagine for some other point it might say class number two.
• 2:42 - 2:48
So of course you have to have already fit the classifier before you
• 2:48 - 2:49
can call predict on it.
• 2:49 - 2:50
Because when it's fitting the data that's where it's
• 2:50 - 2:52
actually learning the patterns.
• 2:52 - 2:55
Then here is where it's using those patterns to make a prediction.
• 2:55 - 2:57
So, that's it.
• 2:57 - 2:59
That's kind of, now you know most all there is to
• 2:59 - 3:02
know to get this working in the first example that I've done.
Title:
Gaussian NB Example - Intro to Machine Learning
Description:

more » « less
Video Language:
English
Team:
Udacity
Project:
ud120 - Intro to Machine Learning
Duration:
03:03
 Udacity Robot edited English subtitles for 02-29 Gaussian_NB_Example Udacity Robot edited English subtitles for 02-29 Gaussian_NB_Example Cogi-Admin edited English subtitles for 02-29 Gaussian_NB_Example

English subtitles

Revisions Compare revisions

• API
Udacity Robot
• API
Udacity Robot
• API