Name: 使用Scikit-learn進行機器學習--使用Python和Pandas進行數據分析第6頁。 (Machine Learning with Scikit-learn - Data Analysis with Python and Pandas p.6)
Uploaded: 2021-01-14T12:27:51.000Z
Duration: 27 min 3 s
Description: 【看影片學英語】數萬部 YouTube 影片，搭配英漢字典即點即查，輕鬆掌握單字發音與用法，長久累積看電影不必再看字幕。

未來進行式

Everybody, welcome to Part six of the Python Data analysis and data Science with Pandas Tutorial Siri's In this last installment, we're going to be talking about applying machine learning to a panda's data frame.

情態副詞

So basically, what is the typical workflow when doing machine learning with pandas data sets.

So generally for if I'm going to do machine learning first I do on my data pre processing in panda's anyways, so it's just convenient to know.

Once we've got our features, how do we feed it through a model?

We're gonna be grabbing a new data set and we're gonna grab the diamonds data set.

Well, we're going to be predicting the price of diamonds, So basically, this table contains a bunch of values here.

We've got, uh, the carrot cut color clarity depth table.

I don't know what that means, but it's table percent.

We got the price, the X y Z, which is length with in depth of that diamond.

So the curiosity is can we take all of those values except for price, feed those through a regression model and predict the price of that diamond so that in the future, when we get diamonds and we don't know how much to pay for our diamonds Ah, we could just run him through a model.

And the question is, are these features all descriptive enough to give us the price of this diamond?

Uh, you can use another day to see if you'd like.

You can, uh this one has almost 54,000 rose, which is quite a few samples that we could actually work with here, which is pretty important for traditional ml.

You're gonna want prime more than 10,000 rose, I would think.

And then, if you want to do, like, deep learning, more than 100,000 arrows were Sydney's regular machine learning models.

But if you want to learn more about what we're doing, one of their more about machine learning or just psych it learn or just basic ML models in General, you can check out this initial machine learning to toil.

This one, basically what we do is we go through starting with regression, which is what we're gonna do here talking about how regression works.

We do an applied example using psychic learn.

And then we actually write a regression model ourselves.

So we do that with regression K nearest neighbors support vector machines and so on.

So the idea is to learn about the model Learned how to apply It was psychic learned.

And then how do we write that ourselves from scratch?

We do use numb pie and stuff like that, but not using some sort of machine learning library.

Serious than if you wanna learn about deep learning.

So anyway, if you want to learn more about that kind of stuff and parameters because there's a lot of parameters here that we're gonna be working with, And if you want to know Maur like you don't feel like like you feel like you're still kind of in the gray areas for us.

What all this stuff means you can check that out.

So now what we want to do is let's say you just are a complete amateur.

You can check out this choosing the right estimator chart.

Basically, if you just Google choosing the right estimator, you will find this.

Uh, this is from psychic learned, by the way.

And this is just how you can pick a specific estimator for a nester class fireman.

How do you pick the right one for what you're working with now?

So because we're doing a brush and we want to predict price, which is a regression task as opposed to classification, where you're trying to predict, you know, one out of five classes in this case it's not classification, because it's like any price, right?

We're just We're trying to come up with some sort of calculation for price.

If we if no, then So if we have a lot of samples, that's kind of confusing.

Otherwise, you should go either here or with support.

Vector bird Russian with a linear kernel.

And again if you don't know what that is, you just click on it, right, And this will tell you.

We then we also want to say index call equal zero because this data set graces us with a love That's avocado with a lovely index.

Not only is it useless, it's also in string form.

So we're gonna say next call equal zero, because that way we don't generate duplicate indexes.

In this case, the index column is completely useless, but one thing you always want to keep.

Take note of is let's go ahead and just d f dot head here.

Any time you're doing machine learning, it's really easy to cheat.

So looking at this data set, it's easy to cheat.

Anyway, looking at this day set, Um, basically, we like all of the columns here are meaningful columns, and then you have price except price.

President will prices meaningful as well, But prices were trying to predict.

So when we go to build this model, we actually want to use all of the columns and sent for price.

But when we get to that point, I'm gonna point something out.

Uh, and, uh, if I forget someone comment below because it's important.

Now, one thing with machine learning is all of the data that you pass into your model at the end of the day.

Basically, all machine learning is is linear algebra.