Decision Tree Parameters - Intro to Machine Learning Udacity English Japanese

Decision Tree Parameters - Intro to Machine LearningYouTube↗ IA↗(long shot)
Education 45.7K views 61 likes 3 dislikes 23 Feb 2015

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To understand what parameters I have available to me,

それでは利用可能なパラメータを理解するために、

going back to the sklearn documentation.

SK Learnの説明書きを見てみます。

So this should look kind of familiar.

前に見たことがあります。

We found this through Google, remember?

Googleで見つけてありますよね。

I am going to scroll down a little bit until I find the DecisionTreeClassifier

少し下にスクロールをしながら決定木の

this being documented here and

分類子を探して、このドキュメントの

I am just going to click on this link.

リンクをクリックします。

And, this takes me to the specific page that's about the DecisionTreeClassifier.

するとDecisionTreeClassifierに関するページに移動します。

And this box right here is what I'm looking for.

この箱に探している情報が記載されます。

This is going to tell me all of the parameters that I

分類子の作成に利用できるすべてのパラメータが出ています。

have available to me when I'm creating my classifier.

開発する分類木が準備できていて、

So, I can see that there's a whole lot that's going on here that I can tune.

いろいろなパラメータをチューニングできることがわかります。

There's something called the criterion, there's the splitter, there's max depth.

criterionやsplitter、max depthなど。

There's minimum number of samples in the split, minimum samples in the leaf, and

Splitにおける最小サンプル数、Leafの最小サンプル数などもあります。

so on and so on.

いろいろありますね。

So, these are all things that I can try tuning.

これらすべてでチューニングを試みることができます。

So, let me pick one of these.

それでは1つ選んでみましょう。

We're going to look at the min's sample split.

min_sample_splitです。

We can see if by tuning the min_sample_split,

min_sample_splitをトレーニングして、

maybe, we can alleviate some of that overfitting that we

さっきお見せした例での過適合を緩和できるか見てみましょう。

were seeing in the example that I just showed you.

ではこのサンプルを使います。

So, here's what min_sample_split does.

まずmin_sample_splitとは何でしょう。

Say, I have my decision tree.

ある決定木があります。

Start out with a bunch of training examples,

まず始めの一連のトレーニングの例を

then I start to split them into smaller sub-samples.

分岐を行い、より小さなサブサンプルに分割します。

At some point, I have to figure out if I'm going to keep splitting any further.

そのうち分裂を続行するべきか決めなければなりません。

So, for example, if I start out with 100 examples here,

例えば、ここで100で始めて、

then maybe I get 60 and 40.

60と40に分割したとします。

And maybe, I get 40 and 20.

次に40と20に分けます。

And then, maybe, this 20 turns into 15.

そして、この20は15になり、

And, five and so on and so on.

5になり、、、などなどです。

So, the question is for each one of these bottom most sort of

ここで問題になるのは、木の最も下の層において

layers in the tree whether I want to keep splitting it.

それぞれ分割を続けるかどうかです。

If it seems like that might be a good idea.

続けたほうがいいかも知れません。

And, that's what min_samples_split governs is basically whether I

min-samples-splitは、基本的に分割を

can keep splitting or not.

続けるかどうかを決定するパラメータです。

Whether there's enough samples that are available to me to continue to

分割を続けるための十分なサンプル数は

あるでしょうか。

So, the the default number for min-sample-split is two.

min-sample-splitのデフォルト数は2です。

So, all of these nodes I can further split except for one of them.

これらのノードは1つを除いてすべて分割が続行可能です。

So my question for you is which of these nodes

それでは質問です。ここにノードがありますので、

here are a few of your choices would I not be allowed to split any further?

分割を続けることのできないノードはどれでしょうか？

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