April 2025
Intermediate
240 pages
6h 56m
Japanese
Content preview from AI開発力を鍛える！機械学習と最適化による問題解決講座
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1
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2
2.7
logを取るべきか
取らざるべきか？
正規化と並び、よく用いられるデータ前処理のためのデータ変換がlog変換
です。本節では、機械学習の入力パラメータ
と出力変数
のそれぞれの
log
変換について、その意味と効果を考えます。
入 力 パラメ ー タ の 変換について
log
変換は数式では次式のように表されます。
なお本書では、自然対数を と書き、常用対数を と書くことにしま
す。
log
変換は、値が小さいところのスケールは引き延ばし、値が大きいところの
スケールは縮小します。その効果として、偏ったデータを均一にならすことがで
きます。
図 2.7.1 に、
の値が低い領域に分布が偏ったデータに対して、
log
変換
を行った結果を示します。データの偏りが緩和され、
に対してデータ点が均一
に分布していることがわかります。一般的に、機械学習モデルの予測精度は、
データ数が同じであった場合、パラメータ空間にデータが均一に分布している方
が全体での予測精度は高まります。したがって、
log
変換によって入力パラメータ
空間におけるデータの偏りを補正してならすことで、得られる機械学習モデルの
精度向上が期待できます。また、データの密度が均一になりますので、2.5節で
議論した、データ密度が低い領域のデータの方が機械学習結果への影響が大きい
という問題を緩和することもできます。
また
log
変換はデータ密度の偏りを補正する以外に、細かくモデル化したい領
域を拡大することができます。
図 2.7.2 に、 ...
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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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.