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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286
13
章 時系列クラスタリング
テストセットでは調整ランド指標はかなり低く、ほとんど
0
だ。つまりクラスタ割り当てはランダム
に行った場合と変わらない。類似性に基づいたグループ分けはうまくいかなかった。
Adjusted Rand Index on Test Set: 0.0006332050676187496
訓練セットが十分に大きければ、
k-Shape
法を用いた時系列クラスタリングモデルのテストセットで
の性能ももう少し良くなるはずだ。
13.4
k-Shape
法による時系列クラスタリング：
ECG5000
ECGFiveDays
データセットの訓練セットにはわずか
23
の観測点しかなく、テストセットにも
861
の
観測点しかない。もっと大きな心電図データセットを使ってみよう。
ECG5000
データセット（これも
UCR Time Series Archive
にある）には、訓練セットとテストセットを合わせて
5,000
の 心 電 図（ つ ま り
時系列データ）がある。
13.4.1
データの準備
データセットをロードして、独自の方法で訓練セットとテストセットに分割し直そう。
5,000
のうち
80%
を訓練セットに、
20%
をテストセットとする。訓練セットがはるかに大きくなったので、時系列ク
ラスタリングモデルの性能もはるかに良くなるはずだ。訓練セットに対しても性能は向上するだろうが、
最も重要なのはテストセットに対する性能向上だ。
# Load the datasets
current_path = os.getcwd() ...
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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.