Gaussian NB Example Udacity English Japanese

Gaussian NB ExampleYouTube↗ IA↗(long shot)
Education 50.3K views 80 likes 19 dislikes 23 Feb 2015

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Okay, so now what I've done is I've gone to the Gaussian Naive Bayes

それではGaussian Naive Bayesの

documentation page.

ドキュメントを読んでみましょう。

sklearn.naive_bayes.GaussianNB.

"sklearn.naive_bayes.GaussianNB"です。

This was the algorithm that I set out to find and

これは探していたアルゴリズムの名前で、

now I've found the sklearn documentation page.

検索した結果sklearnのドキュメントを見つけました。

So the first thing that I see right here, actually this is one of the things I

最初に見るのは、そしてsklearnのドキュメントの素晴らしいところですが

love about the sklearn documentation, is it's full of examples.

たくさんの使用例が記載されています。

When I was actually developing the code for this class, this was one of

私は実際にコードを書くときは

the first things that I would always do is I would come find the example code

いつでもまず最初にサンプルコードを探し出して

and I would try to just run in my Python interpreter,

それから自分のpythonインタプリタで実行し

see if I could get it working.

それらがちゃんと動作するか確認するようにしています。

And almost invariably it works right out of the box.

そしてたいていの場合それはうまく動きます。

So here's something that's just very simple.

さて、ここにとてもシンプルなサンプルがありますね。

There's only a few lines here that are really important.

ここに何行かありますが、これはとても重要です。

So let me point them out to you and

ですから少し着目して

then I'll show you the code I've actually written for the example we just saw,

実際にサンプルコードを書いて動くところをお見せしましょう。

and you'll start to recognize some of these lines.

コードの意味が理解できると思います。

But first let's introduce them.

最初にライブラリをimportします。

So the first one that's really important is this one right here.

ですからここ、この１行はとても重要です。

Above this it's just creating some training points that we can use,

この上のコードは、学習用のデータを生成しているだけです。

it's not that important.

なのでそれほど重要ではありません。

This is where the real meat starts, is with this import statement and if you've

本題はこのimport文から始まります。

programmed in Python before, you're well acquainted with import statements.

pythonプログラミングの経験があれば、import文は知っていますね。

This is the way that you bring in external modules into the code that you're

importは外部のライブラリを使うための構文です。

writing so that you don't have to completely re-implement everything every time,

既に存在するプログラムを再作成する無駄を省き、

you can use code that someone else has already written.

誰かが作ってくれたコードを使い回すことができます。

So we say from sklearn.naive_bayes going to import GaussianNB.

ここではsklearn.naive_bayesからGaussianNBライブラリを使います、と宣言しています。

Very good.

いいですね。

The next thing that we're going to do is we're going to use that to

次に私たちがやろうとしていることは

create a classifier.

分類器の作成です。

So classifier (clf) equals GaussianNB.

分類器はGaussianNBです。

If you miss your import statement.

import文が失敗していると

If you forget this line for

この行を何かの拍子に書き忘れたりすると

some reason, then this line is going to throw an error.

この行でエラーが出ます。

So if you end up seeing some kind of error that says that it doesn't recognize

もし実行時にエラーが出て、このファンクションを

this function.

認識できませんと言ったメッセージなら

It's probably a problem with your import statement.

import文にミスがあります。

So, okay, we've created our classifier.

OK、分類器を作成しました。

So now the code is all sort of ready to go.

これで準備は万端です。

The next thing that we need to do is we need to fit it.

次に私たちがしなくてはいけないのは分類器の"fit"です。

And we've been using the word train interchangeably with fit.

私たちは訓練するという言葉の代わりに"fit"を使います。

So this is where we actually give it the training data,

つまりここで実際に学習用データを与え

and it learns the patterns.

パターンを学習させているのです。

So we have the classifier that we just created.

さっき作成した分類器の

We're calling the fit function on it, and then the two arguments that we pass to

fit() ファンクションを実行します。渡す引数は二つです。

fit are X, which in this case are the features and Y which are the labels.

引数の X は feature 、そして Y は label です。

This is always going to be true in supervised classification.

"supervised classification"においては共通なのですが

is that it's going to call this fit function and

fit() ファンクションを実行し

then it's going to have the features.

引数にfeatureと

And then the labels.

labelを渡します。

And then the last thing that we do is we ask the classifier that

そして最後に、学習させたばかりの分類器に

we've just trained for some predictions.

推定をさせています。

So we give it a new point.

新しいデータを与えてみます。

In this case the point is negative 0.8, negative 1.

与えるデータは [-0.8, -1] です。

And we ask for this what do you think the label is for this particular point?

皆さんはこのデータの label はなんだと思いますか。

What's the, what class does it belong to?

つまりどの分類に所属すると思いますか。

So in this particular case it says it belongs to class number one.

分類器は「１」の分類だと推定しました。

Or you could imagine for some other point it might say class number two.

では「２」の分類だと推定されるデータを思いつきますか。

So of course you have to have already fit the classifier before you

もちろん分類器に推定をさせる前には

can call predict on it.

分類器を fit させておく必要があります。

Because when it's fitting the data that's where it's

データに対してfitを行なっている時が、

actually learning the patterns.

実際にパターンを学習している部分だからです。

Then here is where it's using those patterns to make a prediction.

そしてここは学習したパターンを使って推定を行なっている部分です。

So, that's it.

というわけで、

That's kind of, now you know most all there is to

このコードの動作について知っておくべきことを説明しました。

know to get this working in the first example that I've done.

これが機械学習の最初のサンプルコードです。

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