April 2020
Intermediate to advanced
344 pages
8h 22m
Japanese
Content preview from Pythonではじめる教師なし学習 ―機械学習の可能性を広げるラベルなしデータの利用
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5.5
DBSCAN
133
14 0.553617
15 0.720183
16 0.538891
17 0.484590
18 0.957732
19 0.977310
dtype: float64
ラベルを使っていないことを考慮に入れると、全体的には階層クラスタリングは
MNIST
数字データ
セットに対してかなり高い性能を示していると言えるだろう。
この手順を実際に応用するには次のようにする。まず（
PCA
などで）次元削減を行い、次に（階層ク
ラスタリングなどで）クラスタリングを行い、最後にクラスタごとに、数点を選んでラベル付けを行う。
例えば
MNIST
数字データセットにラベルがまったく付いていなかったとしよう。クラスタごとに数点
の画像を見て、画像に表示されている数字でラベルを与える。クラスタが十分に同質なのであれば、
画像に付けたラベルを、その画像が含まれているクラスタに属するすべての画像に自動的に付けるこ
とができる。
すると、あっという間に大した苦労もなく、
50,000
個のデータセットに対して、およそ
77%
の精度
でラベル付けできる。これは素晴らしいことで、教師なし学習の威力を示すものだ。
5.5
DBSCAN
3
つの広く用いられているクラスタリングアルゴリズムのうち、最後に残った DBSCAN（
density-
based spatial clustering of applications with noise
）に話を進めよう。名前からもわかるように、このク
ラスタリングアルゴリズムは点の密度に基づいてクラスタ ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.