Name: Visualizing a Decision Tree - Machine Learning Recipes #2
Uploaded: 2017-07-16T13:23:59.000Z
Duration: 6 min 42 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

Past simple

Last episode, we used a decision tree as our classifier.

To-infinitive

so we can see how it works under the hood.

you may have heard of before-- things like neural nets

So why did we use a decision tree to start?

In fact, they're one of the few models that are interpretable,

where you can understand exactly why the classifier makes

to a real data set we'll work with today.

Iris is a classic machine learning problem.

In it, you want to identify what type of flower

you have based on different measurements,

The data set includes three different types of flowers.

They're all species of iris-- setosa, versicolor,

given 50 examples of each type, so 150 examples total.

These are the length and width of the sepal and petal.

And just like in our apples and oranges problem,

the first four columns give the features and the last column

gives the labels, which is the type of flower in each row.

Our goal is to use this data set to train a classifier.

Then we can use that classifier to predict what species

of flower we have if we're given a new flower that we've never

Knowing how to work with an existing data set

is a good skill, so let's import Iris into scikit-learn

Conveniently, the friendly folks at scikit

We can import Iris into our code like this.

The metadata tells you the names of the features

and the names of different types of flowers.

For example, if I print out the first entry,

you can see the measurements for this flower.

These index to the feature names, so the first value

refers to the sepal length, and the second to sepal width,

Likewise, these index to the target names.

you'll notice that we just printed out the first row.

Now both the data and target variables have 150 entries.

to print out the entire data set like this.

Now that we know how to work with the data set,

But before we do that, first we need to split up the data.

I'm going to remove several of the examples

We'll call the examples I'm putting aside our testing data.

We'll keep these separate from our training data,

and later on we'll use our testing examples

Testing is actually a really important part

of doing machine learning well in practice,

and we'll cover it in more detail in a future episode.

Just for this exercise, I'll remove one example

ordered so the first setosa is at index 0,

and the first versicolor is at 50, and so on.

The syntax looks a little bit complicated, but all I'm doing

is removing three entries from the data and target variables.

Then I'll create two new sets of variables-- one

Training will have the majority of our data,

and testing will have just the examples I removed.

Now, just as before, we can create a decision tree

classifier and train it on our training data.

Before we visualize it, let's use the tree

and we can print out the labels we expect.

We'll give it the features for our testing data,

You can see the predicted labels match our testing data.

Now, keep in mind, this was a very simple test,

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Visualizing a Decision Tree - Machine Learning Recipes #2

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