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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24
1
章
Python
ではじめる機械学習
タを用いた学習を行っていないため、「学習モデルが新しいデータについてどう振る舞うか」といった、
より現実的な視点で評価できます。
変化点以降（3.5週以降）のデータを使用して学習したモデルに対して、テストデータを用いて誤差
を計算します。
frac = 0.3 #
テストに用いるデータの割合
split_idx = int(frac * len(xb))
#
全データの
30%
をランダムに選び出す
shuffled = sp.random.permutation(list(range(len(xb))))
test = sorted(shuffled[:split_idx]) #
テスト用のデータインデックス配列
train = sorted(shuffled[split_idx:]) #
訓練用のデータインデックス配列
#
それぞれ訓練データを用いて訓練を行う
fbt1 = sp.poly1d(sp.polyfit(xb[train], yb[train], 1))
fbt2 = sp.poly1d(sp.polyfit(xb[train], yb[train], 2))
fbt3 = sp.poly1d(sp.polyfit(xb[train], yb[train], 3))
fbt10 = sp.poly1d(sp.polyfit(xb[train], yb[train], 10))
fbt100 = sp.poly1d(sp.polyfit(xb[train], ...
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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.