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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.
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 sample is only considered out-of-bag for the trees that do not include it in their bootstrap sample.
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 ...
The bootstrapped dataset helps remove the bias that occurs when building a decision tree model with the same data the model is tested with. The ability to leverage the power of random forests can also help significantly improve the overall accuracy of the model being built. This method generates many decisions from many decision trees and ...
[1] [2] When a decision tree is the weak learner, the resulting algorithm is called gradient-boosted trees; it usually outperforms random forest. [1] As with other boosting methods, a gradient-boosted trees model is built in stages, but it generalizes the other methods by allowing optimization of an arbitrary differentiable loss function.
In statistics, jackknife variance estimates for random forest are a way to estimate the variance in random forest models, in order to eliminate the bootstrap effects.
The Italian statistician Corrado Gini developed the Gini coefficient and published it in his 1912 paper Variabilità e mutabilità (English: variability and mutability). [16] [17] Building on the work of American economist Max Lorenz, Gini proposed using the difference between the hypothetical straight line depicting perfect equality and the actual line depicting people's incomes as a measure ...
in a set of N predictions, the Brier score measures the mean squared difference between: The predicted probability assigned to the possible outcomes for item i; The actual outcome ; Therefore, the lower the Brier score is for a set of predictions, the better the predictions are calibrated. Note that the Brier score, in its most common ...