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素人がDeep Learningと他の機械学習の性能を比較してみた

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Machine Learning Casual Talk #2 LT

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素人がDeep Learningと他の機械学習の性能を比較してみた

  1. 1. 誰よお前? •ツイッターID:@MOTIVIC_ •株式会社ALBERT主任研究員 •代数幾何を使って因果推論の研究をしてるよ •統計ゆるふわ勢 •機械学習はICMLにリジェクトくらったレベルの素人 かじゅある ゆるふわツイートしてます
  2. 2. 2012年位からDEEP LEARNINGが流行ってますね 前回のMLCTのLTでも出てましたし
  3. 3. 特徴量抽出も自動でやってくれるので 画像、音声、言語などの分野で大活躍 しているようで
  4. 4. でも画像、音声、言語 のようなデータは扱っ たことがないから凄さ がよく分からないお…
  5. 5. だからもっと簡単な (DEEPLEARNINGが そもそも必要無さそう な)データセットで性能 を調べてみたお!
  6. 6. まずは分類問題のHELLO WORLD IRISデータ
  7. 7. KPIは? •2-FOLDCROSS VALIDATIONでエラー率を計算 •ランダムにデータを半分に分割して上記の計算を 5,000回繰り返した •RとH2Oでシミュレーションしたよ
  8. 8. 比較した手法 •DEEPLEARNING(DL) •CART •RANDOMFOREST(RF) •EXTREMELY RANDOMIZED TREES (ET) •SVM •NEURAL NETWORK(NN)
  9. 9. DEEP LEARNINGのパラメータはどう決めたんだよ? 活性化関数と DROPOUTの部分 だけイジイジして他 はデフォルトだお
  10. 10. 結果
  11. 11. BREAST CANCER データ
  12. 12. BREASTCANCERデータ •サンプルサイズは683 •データはこんな感じ Clump Thickness Uniformity of Cell Size Uniformity of Cell Shape Marginal Adhesion Single Epithelial Cell Size Bare Nuclei Bland Chromatin Normal Nucleoli Mitoses Class 1-10 1-10 1-10 1-10 1-10 1-10 1-10 1-10 1-10 良性/ 悪性
  13. 13. 結果
  14. 14. まとめ •DEEP LEARNINGは簡単な分類問題でも性能が良かった •まじめにチューニングすればもっと性能上がると思うよ

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