… 1 2 n A B V W C X 1 2 … n A … V … W … C … A X feature 1 feature 2

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Presentation transcript:

… 1 2 n A B V W C X 1 2 … n A … V … W … C … A X feature 1 feature 2 feature n target … 1 2 n A B V W C X „Root node feature“ feature 1 feature 2 … feature n target 1 2 … n A … V … W … C … A X „Root node feature“ Altering of one instance leads to different tree feature 2 V W …

X X = Average score X = Single scores

Majority Vote Aggregation Root node = f1 Root node = f11 Original data set f1 f2 f3 fn y … f1 f10 f3 y f5 f11 y f4 f9 y f2 f7 f6 y Boosted_Subsampled_1 Boosted_Subsampled_2 Boosted_Subsampled_3 Boosted_Subsampled_4 Root node = f1 Root node = f11 Root node = f4 Root node = f2 Subspace sampling Split …. Leaf node Subspace sampling Split …. Leaf node Subspace sampling Split …. Leaf node Subspace sampling Split …. Leaf node Aggregation Majority Vote

Random Forest Tree model