道具としての機械学習：直感的概要とその実際

道具としての機械学習：
直感的概要とその実際
北北海道⼤大学情報科学研究科　瀧川⼀一学

⾃自⼰己紹介
北北⼤大で学ぶ (10年年) 内訳: ⼤大学4,⼤大学院2+3,研究員1
😊⾳音響信号の分離離技術 (多変量量解析・統計的信号処理理)
北北⼤大で働く (5年年) 内訳: 特任助教2.5, 准教授2.5
😆離離散構造を伴う機械学習、科学への機械学習の適⽤用
化学研究所・バイオインフォマティクスセンター
薬学研究科・医薬創成情報科学専攻
京⼤大で働く (7年年）内訳: 助教7
😊⽣生命科学データからの知識識発⾒見見・計算⽣生物学

現在の所属 (2017年年3⽉月)
北北海道⼤大学・情報科学研究科
http://art.ist.hokudai.ac.jp
参考) 1974 Turing Award Lecture “Computer Programming as an Art” (Don Knuth)
⼤大規模知識識処理理研究室: 湊真⼀一教授・瀧川(准教授)・⽯石畠正和特任助教
ScienceとEngineeringを
つなぐ「Art」を求めて
https://doi.org/10.1145/361604.361612

1.離離散構造を伴う機械学習
•グラフ集合上の機械学習 (グラフに値をとる確率率率変数上の機械学習)
•グラフデータからのデータマイニング (グラフマイニング)
•ネットワーク構造を持つ因⼦子を伴う機械学習・データマイニング
•組合せ構造を伴う機械学習・データマイニング
2.科学データに対する機械学習
•有機低分⼦子の物性予測のための多次元配列列上の深層学習
•物性・活性の機械学習による予測と評価
•科学データの機械学習における記述⼦子の設計・学習・評価
3.バイオインフォマティクスおよび分⼦子⽣生物学
•代謝反応系の遺伝⼦子ネットワークの遺伝⼦子発現モデル
•低分⼦子化合物-‐‑‒標的ネットワークの特徴づけと予測
•分⼦子⽣生物学研究におけるバイオインフォマティクス解析
4.その他
現在の主な研究項⽬目

本⽇日の話題
① データ社会とデータ駆動科学
② 機械学習って何ができるの？
③ 機械学習ってどう使うの？
④ これだけは知らないと誤⽤用につながる注意点
⑤ まとめと蛇⾜足

質問その１
以下の7つのGoogleサービスをいままでに 
⼀一つも使ったことがない⼈人はいますか？
• Google検索索
• Gmail
• Android
• Google Maps
• YouTube
• Google Chrome
• Google Play

質問その２
以下の7つのGoogleサービスにいままでに 
⼀一回もお⾦金金を払ったことがない⼈人はいますか？
• Google検索索
• Gmail
• Android
• Google Maps
• YouTube
• Google Chrome
• Google Play

なぜ無料料で使えるものが多いのか
• この7つは⽉月間の実利利⽤用者数が10億を超える
• 維持だけでも莫⼤大なお⾦金金がかかるはず
Google社 (アルファベット社)
2015年年の売上⾼高：749億8900ドル（約9兆円)
うち、広告売上⾼高：673億9000ドル（約8兆1000億円）
😳

機械学習のニーズ：すべてがデータになる社会
インターネット・スマートデバイス・IoTが関与する業態
• 対象の詳細なデータを⼤大規模に収集することが可能
• 得られるデータを有効利利⽤用しなくてはならない 
→ 広告/商品推薦、マーケティング、消費者⾏行行動理理解
「情報技術 (分析担当者の仕事の⾃自動化)」の必要性
• 効率率率化：そもそも⼈人⼿手でさばける規模を超えている
• 合理理化：“マイサイド・バイアス (確証バイアス)” 
　　　　⼈人はデータに⾃自分の⾒見見たいものを⾒見見てしまう

広告、マーケティング、消費者⾏行行動理理解…?
真理理。しかし、新しい技術はまず最も⼿手軽に稼げると
ころに導⼊入されるもの。最初に印刷技術で印刷された
ものは「聖書・宗教冊⼦子」「政治論論説」「ポルノ」
ʻ‘The best minds of my generation are thinking
about how to make people click ads… That sucks.ʼ’
Jeﬀ Hammerbacher
機械学習を始めとする情報利利活⽤用技術はかなり汎⽤用的な
ものであり、まだまだ広い適⽤用可能性を持つ！

データ⼤大氾濫濫と情報過多
• このトレンドは科学研究の世界にも到来
• とにかく⾝身の回りでいろいろな情報がデータになり 
もはや⼈人⼿手では利利活⽤用できない…
• “Data Deluge” (データの⼤大氾濫濫)
• ”Information Overload” (情報過多)
😫
😱
• 計測機器の⾼高精度度・⾼高効率率率・⼤大規模化
• ⽣生命科学分野などのオープンデータベース
• ◯◯インフォマティクスの重要性の増⼤大

科学は「データの⾒見見⽅方」と無縁ではいられない！
• こんなビルをたてても安全なの？
• オスプレイは安全なの？
• この薬を飲めば私の病気は治るの？
• この健康⾷食品⾷食べていれば⻑⾧長⽣生きできるの？
• この⾷食品たべればダイエットできるの？
• この化粧品つけていればすこしでも若若くいられるの？
• 原⼦子⼒力力は安全なの？
• 福島へかえりたいけど安全なの？
• このくらい⼤大きな堤防⽴立立てればもう安⼼心なの？
科学というものには、本来限界があって、広い意味での再現可能の
現象を、⾃自然界から抜き出して、それを統計学的に究明していく、
そういう性質の学問なのである。「科学の⽅方法(中⾕谷宇吉郎郎)」

データ駆動科学 (Data-‐‑‒Driven Sciences)
The grand aim of science is to cover the greatest
number of experimental facts by logical deduction
from the smallest number of hypotheses or axioms.
─── Albert Einstein
experimental factshypotheses/axioms
deduction
induction (or abduction)
ここは現在は優れた科学者(⼈人間)がセンスでやっている
(と⾔言うか、これこそが科学者の腕の⾒見見せ所?)

データ駆動科学 (Data-‐‑‒Driven Sciences)
The grand aim of science is to cover the greatest
number of experimental facts by logical deduction
from the smallest number of hypotheses or axioms.
─── Albert Einstein
experimental factshypotheses/axioms
deduction
induction (or abduction)
「facts」が蓄積されてくれば、このinductionが機能しうる
(優れた科学者の経験と勘はその試⾏行行錯誤で醸成される？)

データ駆動科学の理理想と現実
• “Garbage in, Garbage out” 
→「データで駆動」なのでデータがゴミなら、結果もゴミ
• 多くの実問題はいわゆるビッグデータとは関係がない 
→データ駆動科学では良良くみるとスモールデータ問題が⼤大半 
　(⼀一部業態を除き使えるビッグデータの収集は超⾼高コスト)
• データを無⽬目的にたくさん収集しても普通何も解決しない 
→仮説を持たない⼤大量量のデータ(インターネットやオミクス) 
　からどんな知識識が得られるのか?
データ駆動科学の成功を左右するのは本質的には(モデルや⼿手法
ではなく)「データそれ⾃自⾝身の質、量量、カバレッジ」。だが…

?? ?? ??
現在までに調べられた物質可能な新物質の候補
物性
…
1 2 n
物質1
…
1 2 n
物質2
…
1 2 n
物質N
…
1 2 n
候補1
…
1 2 n
候補2
…
1 2 n
候補M…
物性物性
個別に第⼀一
原理理計算
標準的探索索
…
<
Schrödinger
⽅方程式を解く
事例例：シミュレーションと相補的な技術へ

B
その利利活⽤用(予測)
法則性A
既知データに潜む
法則性の⾃自動的獲得
⽬目指すゴール：データ駆動型の帰納的探索索技術の確⽴立立
Q: どのようなやり⽅方が筋がよく、⾼高速で⾼高精度度な予測を与えるのか？
“演繹的”
“帰納的”
?? ?? ??
現在までに調べられた物質可能な新物質の候補
物性
…
1 2 n
物質1
…
1 2 n
物質2
…
1 2 n
物質N
…
1 2 n
候補1
…
1 2 n
候補2
…
1 2 n
候補M…
物性物性
個別に第⼀一
原理理計算
標準的探索索
…
<
Schrödinger
⽅方程式を解く
事例例：シミュレーションと相補的な技術へ

機械学習とは？
「機械が経験から学習する」機能を実現する情報技術
機械 = コンピュータプログラム
経験 = データ
学習 = いくつかタイプがある
• 教師あり学習：お⼿手本から学習
• 教師なし学習：データ⾃自⾝身のパターンを学習
• 強化学習：試⾏行行錯誤で学習

もう少し詳しく…「学習」のタイプ
• 教師あり学習 (supervised) 
⼊入⼒力力と出⼒力力のたくさんの⾒見見本から⼊入⼒力力→出⼒力力の計算を学習
• 教師なし学習 (unsupervised) 
与えられたデータのみで何とかする学習 
例例) 似ているものをグループ化, 次元削減, 埋め込み, 多様体学習
• 半教師あり学習 (semisupervised) 
上⼆二つの間くらいで⾊色々設定がある 
(教師なしのデータが混じってる、グループ化に⾒見見本ついてる、etc)
• 強化学習 (reinforcement) 
試⾏行行錯誤してみて成功失敗のフィードバックで学習 
例例) ⾚赤ちゃんの歩⾏行行の学習、運動やゲームプレイの上達
今⽇日はこれを説明

機械学習とは？
Q.「機械学習」って⼈人⼯工知能ですか？
• この素朴な質問は⾮非常に微妙なところをついている。
• 「機械学習」も「⼈人⼯工知能」も分野横断的な対象で 
使う⼈人によって意味しているものが結構異異なる。
• 「機械学習」「多変量量解析」「データサイエンス」
「データマイニング」「確率率率モデル」「因果推論論」…
A. 機械学習は⼈人⼯工知能を実現するために必要だと考えら
れている要素技術の⼀一つ。 
関⼼心は狭義には「学習の仕組み」に特化している。

実問題を⽣生きるために：論論理理推論論から統計へ
• その昔「知能」とは単純に「論論理理的思考」を意味した
• ⼈人間が「知能」がないとできないと思っていたタスク
の多くは思ったほど論論理理的ではないことが判明
• すべてを論論理理で厳密に推論論(or 形式化)しようとすると
すぐ組合せ爆発やフレームや暗黙知(常識識)や⾃自⼰己⾔言及
などの問題が⽣生じて実問題が全然解けない
• 蓄積されたデータに潜む法則性を経験則として取り出
して、それをフル活⽤用するほうが実問題が解ける！

事例例：⼿手書き⽂文字認識識
⼊入⼒力力した⼿手書き⽂文字の画像を⼊入⼒力力すると、その⽂文字を出⼒力力
してくれるようなコンピュータプログラムをつくりたい
コンピュータ
プログラム
…しかし、どういうコードを書けばいいのだろう?
⼈人間にはたやすくできる処理理だけど、そもそも我々
⾃自⾝身どうやってこの作業をやっているのか理理路路整然
とは説明できん…
「め」

コンピュータ
プログラム
⼊入⼒力力出⼒力力
計算(computation)
会社の給料料計算も、銀⾏行行の⼝口座管理理も、ミサイルの
弾道計算も、スマホアプリも、プレステのゲームも
「計算」部のロジックは⼈人間が考え、通常は 
プログラミング⾔言語を⽤用いてがしがし記述する
コンピュータプログラムをつくるとは

教師つき学習：お⼿手本からの学習
コンピュータプログラムを作るには⼊入⼒力力から出⼒力力を
得る⼿手順がカンペキにわかってなければいけない。
コンピュータ
プログラム
でも、実際には肝⼼心の⼿手順がよく分からないことが多い
?将棋の盤⾯面勝てる確率率率
?現在の脳波⾒見見ている映像
?フランス語⽇日本語
?⾳音声信号テキスト

教師つき学習＝新しいプログラミングの形
教師つき学習 = ⼊入出⼒力力の多量量の⾒見見本から計算部
のロジックを⾃自動的に構築する技術

教師つき学習のしくみ：統計的法則性の活⽤用
⼀一つの事例例だけ⾒見見てもわからないが多くの事例例に共通して
浮かび上がってくる統計的な法則性を使う
(現在の)機械学習 = データの抽象化 = データ中の統計的
法則性を機械(コンピュータ)に学習させる理理論論
他の例例) スマホのカメラかざしたら⼈人の顔がうつっている 
箇所に枠が表⽰示される機能をどうプログラムする？

統計的に浮かび上がる法則性

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⼊入⼒力力が1次元なら、単に曲線当てはめ問題
コンピュータ
プログラム
⼊入出⼒力力の⾒見見本:
⼊入⼒力力
出⼒力力
⼊入出⼒力力の⾒見見本 
(教師データ)

⼤大事な点：現状の教師つき学習の仕組み
⼿手元にあるデータから「⼿手元にまだないデータ」について語るために 
やっていることは(⾮非常に⾼高次元での)⾼高度度なデータ内挿にすぎない。
外挿も計算はできるが基本的には不不得意(当たらない)
⼊入⼒力力
出⼒力力
(⼀一般にはここが1次元ではなく⾼高次元)
⼊入出⼒力力の⾒見見本 
(教師データ)

教師つき学習には様々なモデルがある
⼊入⼒力力
出⼒力力
外挿内挿
外挿

⼊入⼒力力
出⼒力力
外挿内挿
外挿
モデル１

⼊入⼒力力
出⼒力力
外挿内挿
外挿
モデル１
モデル２

⼊入⼒力力
出⼒力力
外挿内挿
外挿
モデル１
モデル２
モデル３

• どのモデルや⼊入⼒力力表現が良良いかは結局問題依存！
• 対象ごとに様々な定式化やノウハウや技術がある
• 出⼒力力を説明するのに⼗十分な⼊入⼒力力を考える必要がある。
• 基本的には⼊入⼒力力は「決まった⻑⾧長さの」配列列 (⼀一⾒見見そうでなくても)
⼊入⼒力力
出⼒力力
外挿内挿
外挿
モデル１
モデル２
モデル３

1 2 3 4
5 6 7 8
9 10 11 12
1. Plugin Bayes Classifier
2. 1-Nearest Neighboor Method
3. 5-Nearest Neighboor Method
4. 3-Layer Perceptron
5. Support Vector Machine
6. Relevance Vector Machine
7. Bayes Point Machine
8. Gaussian Process Classifier
9. Kernel Discreminant Analysis
10. Regression Tree (CART)
11. Random Forest
12. Gradient Boosting Machine

別の視点：学習とは多数のデータ(経験)の「圧縮」
(⼈人間も機械も…) 時事刻々、曝露露される膨⼤大な情報のすべてを
覚えてはいられない！上⼿手に捨てることがパターン認識識の本質
コンピュータ
プログラム
膨⼤大な⼊入⼒力力-‐‑‒出⼒力力事例例のデータの情報をコンパクトかつ 
⼗十分に表現する計算モデルの形へ「圧縮」！！(ただしLossyに)
例例) ニューラルネットの場合、⼊入⼒力力-‐‑‒出⼒力力の写像を
単純演算の合成関数で表現 (多層計算グラフ分解)

教師つき学習による「パターン認識識」
実世界では全ては厳密には⼀一つ⼀一つは少しづつ違っている
Itʼ’s impossible to model everything! (統計的に捉える)
パターン認識識の⾦金金⾔言 (by 渡辺慧)

• 認識識とは不不要な情報を捨てること

• パターンとはそう⾒見見なすことで⽣生じる

• パターンとはそう⾒見見なすことで⽣生じる
• 特徴抽出・特徴選択が識識別や認識識の本質

教師つき学習を利利活⽤用できる局⾯面
⼊入⼒力力を2乗して1⾜足す 170数字数字13
? 2画像数字
?画像⼈人名
⼿手順はよく分からないが「⼤大量量の⼊入出⼒力力例例がある場合」に 
その⼤大規模データからプログラムを⾃自動構築する技術
機械学習の⼀一つの側⾯面 (教師つき学習)

教師つき学習の⼆二つのタイプ
① 判別・分類 (classiﬁcation)
② 回帰 (regression)
X y
(特徴ベクトル/説明変数) (応答変数/⽬目的変数)
?
yが離離散値 (典型例例は2値)
yes/no、true/false、positive/negative
例例) ⼿手書き⽂文字認識識、脱離離者予測、顔認識識、…
yが連続値
例例) 広告クリック率率率予測、降降⾬雨確率率率予測、…

機械学習の定型：表形式データ(多変量量データ)
X y
(特徴ベクトル/説明変数) (応答変数/⽬目的変数)
?
あやめ品種がく⻑⾧長さがく幅花弁⻑⾧長さ花弁幅

教師つき学習を使う実際の流流れ
Step 1. 要件定義：どういう局⾯面に使えるかを考える
? setona⼊入⼒力力出⼒力力
要件：典型的な事例例についての⼊入出⼒力力⾒見見本が
ある程度度(できればたくさん)利利⽤用できること
実際の原理理は不不明だがともかく事例例データが取れるような
ケースに幅広く適⽤用することができる！
かなり汎⽤用的な設定であり、様々な事例例がこの設定になる

Step 2. 変数設計：何から何を予測するかを考える
あやめ品種がく⻑⾧長さがく幅花弁⻑⾧長さ花弁幅
• ⼊入⼒力力は同⼀一の量量の組からなるベクトル(配列列)
• 出⼒力力は離離散値でも連続値でも良良い
要件：⼊入⼒力力と出⼒力力データを表形式として書けること
特徴ベクトル/説明変数応答変数/⽬目的変数

Step 3. 学習：学習に⽤用いるモデルを決めて (例例えば 
Random Forest)、そのモデルをデータに当てはめる
機械学習モデルはパラメタを 
もっており、データ当てはめ 
を⾏行行うと、このパラメタが
最もデータを良良く説明する値に
調整される。
機械学習の⼈人はこのパラメタ
推定を「学習」と呼ぶ

Step 4. 機械学習の結果の利利活⽤用と評価
• 機械学習の予測がどれくらいうまくいって 
いるかを定量量的に評価 (後述)
• 機械学習の結果を分析して対象についての 
知⾒見見を得る (どの変数が最も効いていたか等)
• 実際に知りたい未知の値を予測してみる
6 3.7 5.2 0.9 ??

参考：汎⽤用ツールと実⽤用時のカスタマイズ
以上の話が汎⽤用ツールとしての基本中の基本だが、実際に⾃自分が 
解きたい問題で特殊設定の考慮が必要な場合は要カスタマイズ
1. ⽋欠損値や外れ値がある (アンケートの無回答、計測時のアーチファクト etc)
2. 故障品 or 正常品の分類のような事例例重みつき問題 
(故障品を正常品とする間違いは、正常品を故障品とする間違い 
より、罪が重い(ペナルティを⾼高くすべき)問題)
3. 故障品 or 正常品の分類のように各クラスの事例例数に⼤大きな偏りがある 
(ほとんどは正常品なので、⼊入⼒力力は常に正常品と予測すれば99%正答できる)
4. 対象現象のメカニズムについてモデルが存在するので、ブラックボックスで
はなくグレーボックスにしたい (データ同化など?)
5. 明⽰示的に特殊制約を⼊入れたい (AさんとBさんは家族で同居しているので 
似たような家電製品を普段使っているはず)
6. 明⽰示的には分からない複数の傾向の混合データであり、その⾮非明⽰示的 
なグループごとに傾向がかなり違う (各⼈人の仕事が休⽇日の⾏行行動と平⽇日の⾏行行動)
7. 出⼒力力値は連続値なのだけど値をとる範囲が制限されている (0〜～1の間とか)
例例)

教師つき学習
X y?
← やり⽅方はいろいろある
代表例例
• 最近隣隣法
• ⼀一般線形化モデル
• 線形回帰 for 回帰
• ロジスティック回帰 for 判別
• カーネル法
• サポートベクトルマシン
• カーネルリッジ回帰
• ガウス過程回帰
• ニューラルネットワーク 
(ディープラーニング含む)
• ⽊木に基づく⼿手法
• アンサンブルに基づく⼿手法

準備
データ準備
学習(1⾏行行!)
結果の 
利利活⽤用
と評価
ほんとはダメ
(無調整)

表の形に⼊入⼒力力(説明変数)と出⼒力力(⽬目的変数)を整理理
することが⼤大事。それができれば適⽤用⾃自体は簡単
説明変数⽬目的変数

④ これだけは知らないと 
　誤⽤用につながる注意点

1. 機械学習の成功はデータの質と量量で決まる
5 6.25 7.5 8.75 10
90112.5135157.5180
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5 6.25 7.5 8.75 10
90112.5135157.5180
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⼿手元にあるデータから「⼿手元にまだないデータ」について語るために 
やっていることは(⾮非常に⾼高次元での)⾼高度度なデータ内挿にすぎない。
2次元でもこの差！⾼高次元ならなおさら！
潜んでいる統計的法
則性が分かる…？？

データが少ない場合、予測結果に信頼性はない。
対象現象について詳細な事前知識識と特別なモデリング 
がないかぎり、使える予測のためにはデータ量量も重要
• 伝統的な統計学理理論論は「正規分布」等の仮定のもと 
⼩小ないサンプルでも精緻な推論論をするための道具 
→ しかし⼀一般には集めたデータの確率率率分布構造は不不明
• 「⼈人⼯工知能」と⾔言うには機械学習のみでは不不⼗十分!?
• One-‐‑‒Shot Learning: ⼀一回だけの経験で学習
• Zero-‐‑‒Shot Learning: ⼀一回も経験してないけど学習

• データが少ない上、⾼高次元の場合、どんな説明も 
できてしまいがちなので、偽相関をつかむ恐れ
• ⾒見見本データにない情報や知⾒見見は⾒見見つからない。 
(過去データに全くない画期的法則性は⾒見見つけられない)
• 統計的原理理に基づくため、⾮非常にレアな現象はそれが 
⼈人間にはいくら興味深いものであっても、無視される 
(統計的淘汰。レア現象を重要視するなら要カスタマイズ)
• ⾒見見本中に似た事例例がほとんどないなら当たらない 
(基本的には「データ内挿」の仕組みなので…)

• データのクレンジング (ゴミを取り除く)、正規化 
(集めてきたデータを⽐比較可能なように変換)、前処理理 
などの丁寧なQC(Quality Control)が⾮非常に⼤大切切
• 説明変数の設計・選択は⾮非常に⼤大切切 
(その説明変数が知りたい⽬目的変数と全く関係なければ 
そもそもどんな最新⼿手法を使っても偽相関しか得られない)
• 多くの教師つき学習は⽬目的変数と説明変数の相関関係 
に基づく⼿手法であり、因果関係を表すとは限らない 
(変数設計がOKかは対象問題に照らし慎重な吟味が必要)

https://xkcd.com/552/
“Correlation does not imply causation”
https://en.wikipedia.org/wiki/Correlation_does_not_imply_causation
「応⽤用統計」や「疫学」の教科書の最初に載っている基本のキ

2. ハイパーパラメタの調整が必要
機械学習モデルの２種類のパラメタ
• パラメタ：与えたデータを良良く説明するように⾃自動調整される
• ハイパーパラメタ：⾃自動調整されないパラメタでユーザが設定
このあたりのデフォルト値が与えられたデータにたいして 
適切切である保証はないので実⽤用時には調整が必要

⼊入⼒力力
出⼒力力
外挿内挿
外挿
例例) 多項式曲線をフィットする場合
パラメタ： (係数)
ハイパーパラメタ： (何次の多項式を当てはめるか)

⼊入⼒力力
出⼒力力
外挿内挿
外挿
モデル２
例例) 多項式曲線をフィットする場合
パラメタ： (係数)
ハイパーパラメタ： (何次の多項式を当てはめるか)

3. 訓練誤差とテスト誤差の差をしっかり認識識する
与えられたデータの⼀一部●をランダムに選び、それらは⼿手に⼊入ら
なかったものと仮定する。●を除外した●だけが⼿手に⼊入ったとい
う体で機械学習して、そのモデル　　　が、事前によけておいた
仮想的な未知⼊入⼒力力●をどれくらい当てられるか、で評価する。
●
● に対する誤差
に対する誤差
① 訓練誤差 
(taining error)
② テスト誤差 
(test error)
①は複雑なモデルを使えば下げられる (単に全記憶すれば容易易にゼロに)
②には問題そのものに起因する⾮非⾃自明な下限がある！
モデルの予測値
観測した真の値
誤差
予測式(モデル)

●
● に対する誤差
に対する誤差
① 訓練誤差 (taining error)
② テスト誤差 (test error)
• 真の関⼼心は②を⼩小さくする⽅方法であって①ではない。
• ①は複雑なモデルを使えば下げられるが②は下げられるとは限
らない！①は訓練データを全記憶すればゼロにできる。
この区別が 
⾮非常に重要

訓練集合、確認集合、テスト集合
ある予測モデルの精度度を測りたい！
⼊入⼒力力ベクトル⽬目的変数
訓練データ
訓練集合(training set)
確認集合(validation set)
テスト集合(test set)
予測モデルのフィッティングに使う 
(モデル訓練)
モデルを選択するための予測誤差 
推定に使う (モデル選択)
モデルの性能(汎化誤差)の推定に 
使う (モデル評価)

4. Cross Validationにより学習結果を評価する
① データが⼗十分に多い場合
⼿手元のデータを訓練集合、確認集合、テスト集合の３つに
分割して、よけておいた確認集合はハイパーパラメタ調整
の評価に、テスト集合は予測精度度の良良し悪しの評価に使う
② データがあまり多くない場合
できるだけ⼿手持ちのデータでなんとかするために
Cross Validation (交差検証)を使うのがコンセンサス。
「⼀一部を避けておき、残ったデータで学習し避けておいた
データを予測してみて実際に当たったか調べる」を何度度か 
繰り返し、予測精度度の平均値で良良し悪しを判断

k-‐‑‒fold CV (k=3の場合)
• yの分布を保存して分割する
場合も (層別k-‐‑‒fold)
• ランダム分割を反復復して平均 
(Bootstrap or Shuﬄe-‐‑‒Split)
1) B2,B3を⽤用いてモデルf1を学習
2) f1の性能をB1を⽤用いて測る
7) 2,4,6の性能の平均を出⼒力力
CVのバリエーション(⾊色々)

ハイパーパラメタ調整の判定の意味に注意！
… …
訓練集合テスト集合
CVでは下記の分割を何度度かやって平均 (確認集合がない)
予測モデル学習予測
❌ このハイパーパラメタ値の良良し悪しの判定に「テスト集合」を使ってはダメ！ 
(もはや⼿手に⼊入らないはずのテスト集合を「学習」してることになりleakyな設定)
…
やるなら、訓練集合にさらに 
CVを適⽤用した検証でハイパー 
パラメタ値を決定！(Double CV)

5. ベースライン予測の構築の重要性
どの評価値がどれくらいになれば成功と⾔言えるのかを
判断できるようにしておくことが重要
• 予測性能の評価尺度度には様々なものがある。 
正答率率率、平均２乗平⽅方根誤差、AUC、R2
、…
• 訓練データのyの分布(レンジや偏り)をチェックしておく。 
２値分類(positive or negativeを予測)の場合、 
positiveが⾮非常に少ない場合、すべてnegativeと答える⾃自明な
予測が採⽤用した評価基準で良良くならないか?
• Xとyの対応をランダムにシャッフルしたときの値と⽐比べる、
回帰の場合でyの平均値による定数予測と⽐比べる、など 
「⾃自明でアホみたいな⽅方法」より有意に良良いかを確認する

準備
データ準備
学習(1⾏行行!)
結果の 
利利活⽤用
と評価
ほんとはダメ
(無調整)
←実⽤用では問題あり
(ハイパーパラメタ未調整)

２つのハイパーパラメタ調整をDouble CVで評価
• ここではハイパーパラメタ評価⽤用のCVは3-‐‑‒foldでサボっているが 
それでも計算時間はかなり増⼤大する (ʻ‘pgグリッドの組合せ×3ʼ’倍)
• 良良い感じの探索索グリッドを決めるためにまずはマニュアル調整す
るなどが必要な場合も多い
学習・調整

⽬目的変数の分布を確認
層別(Stratiﬁcation)の処理理が必要かを把握しておく
(下記は離離散値の例例、連続値(回帰)ならヒストグラムなどで調べる)
データ統計量量(やモデル当てはめ結果)の視覚化も⼤大切切！

⾃自明な予測⼿手法と⽐比較(特に⾮非常に⾼高次元な場合は偽相関性の検証に)
例例1: Y-‐‑‒Scrambling (Xとyをランダムにソートして偽問題を作る)
予測精度度が下がることを確認(もしこの程度度の精度度しか出てなければ
意味のある学習は⾏行行われていない)
RFの場合、この値(accuracy/正答率率率)が96%くらいなのに
対して、Xとyをランダム対応にすると33.3%くらいになる
→ この問題では少なくとも33.3%は出てないと無意味な学習

⾃自明な予測⼿手法と⽐比較(特に⾮非常に⾼高次元な場合は偽相関性の検証に)
予測精度度が下がることを確認(もしこの程度度の精度度しか出てなければ
意味のある学習は⾏行行われていない)
例例2: アホみたいな予測(定数予測やランダム予測)と⽐比較

前⽴立立腺癌細胞株PC3に対する成⻑⾧長阻害アッセイデータ
活性あり(active): 1,737化合物活性なし(inactive): 26,895化合物
https://pubchem.ncbi.nlm.nih.gov/bioassay/41
予測モデル活性(あり or なし)

まとめ
④ これだけは知らないと誤⽤用につながる注意点

「機械学習」は道具である。
• ⼈人間は認知バイアスを持つ。(データの中に⾃自
分の⾒見見たいものを⾒見見てしまう)
• その上、⼈人間は限られた量量の情報しか処理理で
きない。(情報疲弊、思考停⽌止、…)
• でも、データが⼭山のように⽣生成される現状は
ますます酷くなる。
じゃあ、どうする！？
→⼈人間に出来ない情報処理理を⾏行行う「道具」へ

“魂は細部に宿る”
• プロにとって使う道具は命。
• 道具の特性に精通し、丁寧に扱い、
⼿手⼊入れを怠らない。
• 道具箱の中をひとめ⾒見見るだけでその
職⼈人の気質とレベルが分かる。
職⼈人魂
より理理解するために技法(道具)を整備する
「良良い仕事は良良く⼿手⼊入れされた道具から」
http://www.900910.com/mies.php

機械学習の現状とこれから (道具職⼈人の⼀一雑感)
• 現状の技術は統計的法則性を⾒見見つけ、それに基づいて 
予測モデルを構築する⽅方法 (“No Silver Bullet”)
• 使いやすい道具としてコモディティ化しつつある。 
しかし得⼿手不不得⼿手や限界や落落とし⽳穴を認識識して使うべし
• 汎⽤用技術としては「⼈人⼯工知能」を名乗るには 
まだまだ発展途上の研究領領域 (現枠組の拡張研究も含む)
• 問題の定式化・変数設計・データ洗浄・QC・前処理理
など⼈人⼿手のお膳⽴立立てがまだものすごい必要
• 経験的な勘のモデルであって⼈人間の特徴である論論理理的
な思考とのつながりはまだ綺麗麗な説明がない

「機械学習(machine learning)」とは？
経験(データ)から⾃自ら“学習”するようなコンピュータプログラム
を実現するための理理論論と技術
• ⼯工学技術として 
⽂文字/画像認識識、⾳音声認識識、機械翻訳、信号処理理、制御、商品推薦等
• 応⽤用数学として 
⼿手法の定式化・体系化、性質分析、効率率率改善、収束や精度度の保証
• 分析哲学として 
⼈人⼯工知能とは、学習とは、認識識とは、我々(脳)の⾼高次機能とは、…
(個⼈人的⾒見見解) 主に３つの側⾯面/⾯面⽩白さがある
「知識識、認知、⾔言語、運動など、我々はなぜ有限回の経験で 
　何かを学習できる(できた気になる)のだろうか?」
⻑⾧長期的関⼼心：

参考
•科学と機械学習のあいだ：変量量の設計・変換・選択・交互作⽤用・線形性 
第19回情報論論論論的学習理理理理論論論論ワークショップ (IBIS2016), 2016年年11
⽉月18⽇日, 京都⼤大学. 
http://www.slideshare.net/itakigawa/ss-‐‑‒69269618
•データマイニングとしての多重標的相互作⽤用解析 
⽇日本薬学会構造活性相関部会・ニュースレター SAR NEWS No.29. 
http://bukai.pharm.or.jp/bukai_̲kozo/SARNews/SARNews_̲29.pdf
•多数のグラフからの統計的機械学習 
⼈人⼯工知能学会第94回⼈人⼯工知能基本問題研究会招待講演 
http://www.slideshare.net/itakigawa/94-‐‑‒71669875

道具としての機械学習：直感的概要とその実際

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道具としての機械学習：直感的概要とその実際