Random Forests Training System

A Random Forests Training System is a decision tree ensemble learning system that implements an RF training algorithm to solve an RF training task.

• Context:
  • It can range from being a Random Forest Classification System to being a Random Forest Regression System.
• Example(s):
  • sklearn.ensemble.RandomForestRegressor [1].
  • sklearn.ensemble.RandomForestClassifier[2].
  • …
• Counter-Example(s):
  • A Rotation Forests System.
  • An AdaBoost System.
  • A Gradient Tree Boosting System.
  • A Bagged Trees System.
  • An Extremely Randomized Trees System.
  • A Totally Random Trees Embedding System.
  • An Isolation Forest System.
• See: Naive-Bayes Training System.

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References

2017a

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Random_forest Retrieved:2017-10-22.
  • Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set. The first algorithm for random decision forests was created by Tin Kam Ho using the random subspace method, which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg.

    An extension of the algorithm was developed by Leo Breiman and Adele Cutler, and "Random Forests" is their trademark. The extension combines Breiman's “bagging” idea and random selection of features, introduced first by Ho and later independently by Amit and Geman in order to construct a collection of decision trees with controlled variance.

2017b

• (Sammut & Webb, 2017) ⇒ Claude Sammut and Geoffrey I. Webb. (2017). "Random Forests" In: "Encyclopedia of Machine Learning and Data Mining"(Editors: Claude Sammut, Geoffrey I. Webb) pp. 1054-1054
  • QUOTE: Random Forests is an ensemble learning technique. It is a hybrid of the Bagging algorithm and the random subspace method, and uses decision trees as the base classifier. Each tree is constructed from a bootstrap sample from the original dataset. An important point is that the trees are not subjected to pruning after construction, enabling them to be partially overfitted to their own sample of the data. To further diversify the classifiers, at each branch in the tree, the decision of which feature to split on is restricted to a random subset of size n, from the full feature set. The random subset is chosen a new for each branching point. n is suggested to be log2(N + 1), where N is the size of the whole feature set.

2017c

• (Scikit Learn, 2017) ⇒ http://scikit-learn.org/stable/modules/ensemble.html#random-forests Retrieved: 2017-10-22.
  • QUOTE: In random forests (see RandomForestClassifier and RandomForestRegressor classes), each tree in the ensemble is built from a sample drawn with replacement (i.e., a bootstrap sample) from the training set. In addition, when splitting a node during the construction of the tree, the split that is chosen is no longer the best split among all features. Instead, the split that is picked is the best split among a random subset of the features. As a result of this randomness, the bias of the forest usually slightly increases (with respect to the bias of a single non-random tree) but, due to averaging, its variance also decreases, usually more than compensating for the increase in bias, hence yielding an overall better model.

    In contrast to the original publication B2001, the scikit-learn implementation combines classifiers by averaging their probabilistic prediction, instead of letting each classifier vote for a single class.

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