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Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training. For classification tasks, the output of the random forest is the class selected by most trees.
Rotation forest – in which every decision tree is trained by first applying principal component analysis (PCA) on a random subset of the input features. [ 13 ] A special case of a decision tree is a decision list , [ 14 ] which is a one-sided decision tree, so that every internal node has exactly 1 leaf node and exactly 1 internal node as a ...
In a random forest, each tree "votes" on whether or not to classify a sample as positive based on its features. The sample is then classified based on majority vote. An example of this is given in the diagram below, where the four trees in a random forest vote on whether or not a patient with mutations A, B, F, and G has cancer.
One set, the bootstrap sample, is the data chosen to be "in-the-bag" by sampling with replacement. The out-of-bag set is all data not chosen in the sampling process. When this process is repeated, such as when building a random forest, many bootstrap samples and OOB sets are created. The OOB sets can be aggregated into one dataset, but each ...
The query example is classified by each tree. Because three of the four predict the positive class, the ensemble's overall classification is positive. Random forests like the one shown are a common application of bagging. An example of the aggregation process for an ensemble of decision trees.
Evolutionary Forest is a Genetic Programming-based automated feature construction algorithm for symbolic regression. [15] [16] uDSR is a deep learning framework for symbolic optimization tasks [17] dCGP, differentiable Cartesian Genetic Programming in python (free, open source) [18] [19]
For example, processes in the AR(1) model with | | are not stationary because the root of = lies within the unit circle. [3] The augmented Dickey–Fuller test assesses the stability of IMF and trend components. For stationary time series, the ARMA model is used, while for non-stationary series, LSTM models are used to derive abstract features.
Discussions of some more exotic generalizations of random forests. There are a lot of neat, somewhat exotic models which use random forests as a base, but this has the same risk as a list of links. Significantly more examples, similar to sections 3.3,4.3,5.3,6.3,etc of the Criminisi paper I linked above.