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  2. Random forest - Wikipedia

    en.wikipedia.org/wiki/Random_forest

    As with ordinary random forests, they are an ensemble of individual trees, but there are two main differences: (1) each tree is trained using the whole learning sample (rather than a bootstrap sample), and (2) the top-down splitting is randomized: for each feature under consideration, a number of random cut-points are selected, instead of ...

  3. Random subspace method - Wikipedia

    en.wikipedia.org/wiki/Random_subspace_method

    The random subspace method has been used for decision trees; when combined with "ordinary" bagging of decision trees, the resulting models are called random forests. [5] It has also been applied to linear classifiers, [6] support vector machines, [7] nearest neighbours [8] [9] and other types of classifiers.

  4. Out-of-bag error - Wikipedia

    en.wikipedia.org/wiki/Out-of-bag_error

    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 ...

  5. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    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 ...

  6. Random tree - Wikipedia

    en.wikipedia.org/wiki/Random_tree

    In mathematics and computer science, a random tree is a tree or arborescence that is formed by a stochastic process. Types of random trees include: Types of random trees include: Uniform spanning tree , a spanning tree of a given graph in which each different tree is equally likely to be selected

  7. Jackknife variance estimates for random forest - Wikipedia

    en.wikipedia.org/wiki/Jackknife_Variance...

    2 Examples. 3 Modification for bias. ... Download QR code; Print/export ... jackknife variance estimates for random forest are a way to estimate the variance in ...

  8. Tree (graph theory) - Wikipedia

    en.wikipedia.org/wiki/Tree_(graph_theory)

    A forest is an undirected graph in which any two vertices are connected by at most one path, or equivalently an acyclic undirected graph, or equivalently a disjoint union of trees. [ 2 ] A directed tree, [ 3 ] oriented tree, [ 4 ] [ 5 ] polytree , [ 6 ] or singly connected network [ 7 ] is a directed acyclic graph (DAG) whose underlying ...

  9. Disjoint-set data structure - Wikipedia

    en.wikipedia.org/wiki/Disjoint-set_data_structure

    function MakeSet(x) is if x is not already in the forest then x.parent := x x.size := 1 // if nodes store size x.rank := 0 // if nodes store rank end if end function. This operation has linear time complexity. In particular, initializing a disjoint-set forest with n nodes requires O(n) time.