Extra Trees Regression System

An Extra Trees Regression System is a Extra Trees Learning System that implements an Extra Trees Regression Algorithm to solve a Extra Trees Regression Task.

• AKA: Extremely Randomized Trees Regression System, Ensemble Extra Trees Regressor.
• Context:
  • It is an Extra Trees Learning System for solving a Regression problem.
  • …
• Example(s):
  • sklearn.ensemble.ExtraTreesRegressor
• Counter-Example(s):
  • Extra Trees Classification System.
• See: Random Forests Model, Out-of-Bag Error, Partial Permutation, K-Nearest Neighbor Algorithm.

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References

2017

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Random_forest#ExtraTrees Retrieved:2017-11-5.
  • Adding one further step of randomization yields extremely randomized trees, or ExtraTrees. These are trained using bagging and the random subspace method, like in an ordinary random forest, but additionally the top-down splitting in the tree learner is randomized. Instead of computing the locally optimal feature/split combination (based on, e.g., information gain or the Gini impurity), for each feature under consideration, a random value is selected for the split. This value is selected from the feature's empirical range (in the tree's training set, i.e., the bootstrap sample).

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