Name: 讓我們來寫一個管道--機器學習食譜#4。 (Let’s Write a Pipeline - Machine Learning Recipes #4)
Uploaded: 2021-01-14T06:58:27.000Z
Duration: 7 min 54 s
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so today I want to review and reinforce concepts.

Next, we'll build up a little more intuition

for what it means for an algorithm to learn something

from data, because that sounds kind of magical, but it's not.

To kick things off, let's look at a common experiment

Imagine you're building a spam classifier.

That's just a function that labels an incoming email

Now, say you've already collected a data set

there's a question you need to answer first--

how accurate will it be when you use it to classify emails that

As best we can, we want to verify our models work well

And we can do an experiment to help us figure that out.

One approach is to partition our data set into two parts.

and Test to see how accurate it is on new data.

That's a common pattern, so let's see how it looks in code.

To kick things off, let's import a data set into [? SyKit. ?]

We'll use Iris again, because it's handily included.

that I'm calling the features x and the labels y.

Well, that's because one way to think of a classifier

At a high level, you can think of x as the input

I'll talk more about that in the second half of this episode.

After we import the data set, the first thing we want to do

And to do that, we can import a handy utility,

or our features and labels, and partitioning them

X_train and y_train are the features and labels

And X_test and y_test are the features and labels

Here, I'm just saying that I want half the data to be

So if we have 150 examples in Iris, 75 will be in Train

to show you how they accomplish the same task.

Let's start with the decision tree we've already seen.

Now let's train the classifier using our training data.

At this point, it's ready to be used to classify data.

the classifier predicts for each row in the testing data.

Now let's see how accurate our classifier

Recall that up top, we have the true labels for the testing

compare the predicted labels to the true labels,

There's a convenience method in [? Sykit ?]

If you try this on your own, it might be a little bit different

because of some randomness in how the Train/Test

By replacing these two lines, we can use a different classifier

we'll use one called [? KNearestNeighbors. ?]

If we run our experiment, we'll see that the code

The accuracy may be different when you run it,

because this classifier works a little bit differently

and because of the randomness in the Train/Test split.

Likewise, if we wanted to use a more sophisticated classifier,

we could just import it and change these two lines.

The takeaway here is that while there are many different types

of classifiers, at a high level, they have a similar interface.

Now let's talk a little bit more about what

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讓我們來寫一個管道--機器學習食譜#4。 (Let’s Write a Pipeline - Machine Learning Recipes #4)

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