October 2014
Intermediate to advanced
288 pages
6h 27m
Japanese
Content preview from 実践 機械学習システム
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14
1
章
Python
ではじめる機械学習
plt.title("Web traffic over the last month")
plt.xlabel("Time")
plt.ylabel("Hits/hour")
plt.xticks([w*7*24 for w in range(10)],
['week %i'%w for w in range(10)])
plt.autoscale(tight=True)
plt.grid()
plt.show()
得られるグラフは図1-2のようになります。はじめの週では、トラフィック量はおおむね一定です
が、最後の週で急に増加しているのがわかります。
図1-2
1.5.3
正しいモデルの選択と機械学習
データの感じは把握できたので、「今のサーバで、いつまでトラフィックに耐えることができるか？」
という問題について考えてみましょう。そのためには、次の二点を考えなければなりません。
●
ノイズの含まれたデータに対して、その背後にある本当のモデルを見つける
●
そのモデルを用いて、インフラがパンクする時期を予測する
先月のWebトラフィック量
時間
1.5
はじめての（簡単な）機械学習アプリケーション
15
はじめてのモデル構築
本書で「モデル」という言葉が登場するとき、それが示す
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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.