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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訓練・検証・テストデータは
どのように分ければよいか？
機械学習を学んだことがある方ならば、機械学習のお作法として、「データは
訓練・検証・テストの3つに分ける」ということは、骨の髄まで染みついて
いるでしょう。しかし、このデータ分割の工程にも、意外と落とし穴があり
ます。本節では、まずデータ分割についておさらいし、データの意味を考え
たデータ分割について考えてみましょう。
デ ータ 分 割 の おさらい
データ分割は、あらゆるAI入門書にもれなく書かれている、機械学習における
非常に基礎的な事柄です。機械学習では
図 2.8.1 のように、データは訓練データ、
検証データ、テストデータの3つに分けます。
訓練データは学習パラメータの調整に使用します。例えば、線形回帰では各項
の係数が、ニューラルネットワークでは重みとバイアスが、訓練データに対する
損失関数を最小にするように調整（学習）されます。
検証データはハイパーパラメータの調整に使用します。
図 2.8.1 では、Ridge
回帰の損失関数のハイパーパラメータであるαを例示しています。損失関数内の
ハイパーパラメータ以外にも、ニューラルネットワークの隠れ層や隠れユニット
の数といった機械学習アーキテクチャ（処理を行う構造）内のハイパーパラメー
タ、学習率やエポック数といった学習条件のハイパーパラメータなど様々な形式
のハイパーパラメータがあります。検証データは、これらハイパーパラメータを
最適化する際の目的関数の評価に用いられます。 ...
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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.