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Installing scikit-learn using Anaconda
6:07We're going to use a Python library called scikit-learn, which includes lots of well designed tools for performing common machine learning tasks. We're going to install scikit-learn and its dependencies using Anaconda, which is a Python-based platform focused on data science and machine learning.
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We're going to use a Python library
called scikit-learn, which includes lots
0:04
of well designed tools for
performing common machine learning tasks.
0:09
We're going to install scikit-learn and
its dependencies using Anaconda,
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which is a Python based platform focused
on data science and machine learning.
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If you haven't installed Anaconda already,
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check the notes associated with this video
for instructions on how to do that first.
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It's easy and quick to set up,
so if you don't have it yet,
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pause this video and
come back when you have Anaconda running.
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You'll also need to download the project
files associated with this video.
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Inside the ZIP file,
you'll find a file called environment.yml.
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Remember where you save this file, because
you'll need to find it again later.
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Once you have Anaconda Navigator
open on your computer and
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the environment.yml file downloaded,
you're ready to get started.
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Inside Anaconda Navigator,
click the Environments tab on the left.
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And by default, you should have at least
one environment already, called base.
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But to make sure that you have all the
dependencies installed that you'll need,
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I've created an environment for us to use.
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Click the Import button at
the bottom of the environments list.
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Then click the folder
icon on the right side.
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And locate the environment.yml file
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that you saved earlier and click Open.
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The .yml file will suggest a name for
your environment.
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In this case, it's MachineLearningBasics,
which is the name of this course.
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However, you can rename the environment
to something else if you'd prefer,
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when you're done, choose Import, This will
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begin the process of downloading and
installing all the dependencies
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you'll need to use scikit-learn,
including scikit-learn itself.
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This might take a few minutes,
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depending on the speed of your
Internet connection and your computer.
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While that's running,
let's take a look at the documentation for
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scikit-learn to get
a little more familiar.
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Point your web browser
to scikit-learn.org, you
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can find a link in the notes associated
with this video if that's easier for you.
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And, well, you've already seen
the home page in a previous video,
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let's click on Classification.
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This is a list of all the different types
of supervised learning functions and
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algorithms within scikit-learn.
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It's quite a long list and
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it can be challenging to understand
which tools we should use.
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That's not something we'll be
able to get into in this course.
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But as we learned previously,
supervised learning starts with a dataset.
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And that's what we want, so
we know we're in the right place.
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Scroll down to the section labeled 1.10,
Decision Trees.
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Then click the first link underneath that
header that says 1.10.1, Classification.
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DecisionTreeClassifier is
the class that we'll use.
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It says, DecisionTreeClassifier is a class
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capable of performing multi-class
classification on a dataset.
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As with other classifiers,
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DecisionTreeClassifier takes
as input two arrays.
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An array x, sparse or dense,
of size n_samples and
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n_features, holding the training samples.
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And an array y of integer
values size n_samples,
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holding the class labels for
the training samples.
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In other words, a DecisionTreeClassifier
is one type of classifier.
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In this case, it's a model that
tries to predict a value by making
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its own choices and rules that
are inferred from the data features.
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The programming behind this is
pretty complicated, but essentially,
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the computer tries to fit
the data to a pattern.
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And using other methods in
the DecisionTreeClassifier class,
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you can visualize the decision
path of the tree and
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have the computer show how it
arrived at a certain prediction.
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Looking at the example here,
the DecisionTreeClassifier
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starts with a dataset in
the form of an array.
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This data will be all of our examples,
broken out by feature.
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And then we have another array that
contains our labels for the data.
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Then we create
a DecisionTreeClassifier object and
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use its function called fit to build
a DecisionTreeClassifier from the dataset.
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In other words, this is where
the decision tree is created.
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And using that same decision tree,
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we can now predict new examples
that don't have labels.
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That's a lot of information to take in,
and for the sake of keeping this
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overview at a high level,
I've simplified a lot of the details.
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But by now, your environment
should be done installing.
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And in the next video,
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we're going to go over that same
code again by writing it ourselves.
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To learn more right now,
I highly recommend you check out the notes
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associated with this video, and read the
scikit-learn documentation for yourself.
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