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Random Forest (Breiman 2001)

  • Random Forest:
    • Each classifier in the ensemble is a decision tree classifier and is generated using a random selection of attributes at each node to determine the split
    • During classification, each tree votes and the most popular class is returned
  • Two Methods to construct Random Forest:
    • Forest-RI (random input selection): Randomly select, at each node, F attributes as candidates for the split at the node. The CART methodology is used to grow the trees to maximum size
    • Forest-RC (random linear combinations): Creates new attributes (or features) that are a linear combination of the existing attributes (reduces the correlation between individual classifiers)
  • Comparable in accuracy to Adaboost, but more robust to errors and outliers
  • Insensitive to the number of attributes selected for consideration at each split, and faster than bagging or boosting


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