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Feature-Engineeringのリンク集めてみた

Last updated at Posted at 2018-07-31

#【理論】
###Feature Engineering
https://www.slideshare.net/HJvanVeen/feature-engineering-72376750

###Discover Feature Engineering, How to Engineer Features and How to Get Good at It
https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/
###カテゴリカル変数のEncoding手法のまとめ
http://jotkn.ciao.jp/wp/2017/08/22/post-67/
###Feature Engineering: Data scientist's Secret Sauce !
https://www.linkedin.com/pulse/feature-engineering-data-scientists-secret-sauce-ashish-kumar?trk=prof-post

###最近のKaggleに学ぶテーブルデータの特徴量エンジニアリング
https://www.slideshare.net/mlm_kansai/kaggle-138546659

#【実装】
###DataFrameで特徴量作るのめんどくさ過ぎる。。featuretoolsを使って自動生成したろ
https://qiita.com/Hyperion13fleet/items/4eaca365f28049fe11c7

featuretoolsについてはHome Credit Default Riskの公開カーネルでも言及されてました。
Will Koehrsenさんは手動と自動の両方のカーネルを公開しており非常に勉強になる。
###Automated Feature Engineering Basics
https://www.kaggle.com/willkoehrsen/automated-feature-engineering-basics

###Introduction to Manual Feature Engineering
https://www.kaggle.com/willkoehrsen/introduction-to-manual-feature-engineering

###Introduction to Manual Feature Engineering P2
https://www.kaggle.com/willkoehrsen/introduction-to-manual-feature-engineering-p2

###KaggleのWinner solutionにもなった「K近傍を用いた特徴量抽出」のPython実装
https://upura.hatenablog.com/entry/2018/06/23/165855

###カテゴリカル変数のEncoding手法について
http://nami3373.hatenablog.com/entry/2018/07/26/230655

###fast_feng
https://github.com/tomomotofactory/ffeng

###学習アルゴリズム以外のscikit-learn便利機能と連携ライブラリ
https://yubessy.hatenablog.com/entry/2016/02/17/164511

###遺伝的プログラミングによる特徴量生成
https://qiita.com/overlap/items/e7f1077ef8239f454602

###遺伝的プログラミングによる特徴量生成でLightGBMの精度向上
https://upura.hatenablog.com/entry/2018/12/11/000000

#【Coursera】
How to Win a Data Science Competition: Learn from Top Kagglers
https://www.coursera.org/learn/competitive-data-science

#【本】
###特徴量エンジニアリングに焦点を当てた簡潔な本:「Feature Engineering for Machine Learning」
https://10001ideas.com/2018/07/19/%E7%89%B9%E5%BE%B4%E9%87%8F%E3%82%A8%E3%83%B3%E3%82%B8%E3%83%8B%E3%82%A2%E3%83%AA%E3%83%B3%E3%82%B0%E3%81%AB%E7%84%A6%E7%82%B9%E3%82%92%E5%BD%93%E3%81%A6%E3%81%9F%E7%B0%A1%E6%BD%94%E3%81%AA%E6%9C%AC/

2月23日にオライリーから出版予定です。
[機械学習のための特徴量エンジニアリング ―その原理とPythonによる実践][1]
[1]:https://www.amazon.co.jp/dp/4873118689/ref=as_li_ss_tl?ie=UTF8&qid=1547182598&sr=8-2&keywords=Python%E7%89%B9%E5%BE%B4%E9%87%8F%E3%82%A8%E3%83%B3%E3%82%B8%E3%83%8B%E3%82%A2%E3%83%AA%E3%83%B3%E3%82%B0&linkCode=sl1&tag=hoxom023-22&linkId=b69947b517c075acb5a12e6a41e08993&language=ja_JP

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