Random Forests Model

A Random Forests Model is an ensemble decision tree model (composed of decisions tree models) produced by a random forest training system.

• Context:
  • It can range from being a Random Forests Classification Model to being a Random Forests Ranking Model to being a Random Forests Regression Model.
• See: Gradient Boosted Tree Model.

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References

2001

• (Breiman, 2001) ⇒ Leo Breiman. (2001). “Random Forests.” In: Machine Learning, 45(1). doi:10.1023/A:1010933404324
  • QUOTE: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges as to a limit as the number of trees in the forest becomes large.

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