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アンサンブル学習.pptx
1.
アンサンブル学習
2.
アンサンブル学習 • 複数のモデルを学習,多数決を取って,Predictとする • アンサンブル学習は,モデルの相関が小さくなければならない •
種類 • バギング • ブースティング • スタッキング
3.
バギング(bootstrap aggregating) • 母集団から重複を許してランダムにデータを選択 例)母集団{1,
2, 3, 4, 5} サンプル群1:{1, 2, 2}⇒ モデル1 サンプル群2:{4, 3, 3}⇒ モデル2 サンプル群3:{2, 5, 1}⇒ モデル3 サンプル群4:{1, 4, 1}⇒ モデル4 アンサンブル 回帰:平均値 分類:多数決
4.
ブースティング(boosting) 1. 学習 2. 1で正しく分類できていない学 習データに対して,重みを大き く設定する 3.
Training Lossが閾値より高い ⇒ 1へ 4. Training Lossが閾値より低い ⇒ 終了 モデル1 結果1 モデル2 上手く学習できなかった 学習データに対して重み付け 結果2 モデル2 結果3 アンサンブル
5.
スタッキング(stacking) • 複数のモデルの出力を特徴量と捉え,その特徴量を入力として, 最終的なPredictを出力するモデルを作成する 結果1 結果2 結果3 結果4 結果5 バギング等の出力に対して これを更にモデルに入れる モデル 最終的な出力
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