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Fast ML tree reconstruction, bootstrap analysis, model selection, hypothesis testing, tree calibration, tree manipulation and visualization, computation of sitewise rates, sequence simulation, many models of evolution (DNA, protein, rRNA, mixed protein, user-definable), GUI and scripting language: Maximum likelihood, distances, and others
A tree structure, tree diagram, or tree model is a way of representing the hierarchical nature of a structure in a graphical form. It is named a "tree structure" because the classic representation resembles a tree, although the chart is generally upside down compared to a biological tree, with the "stem" at the top and the "leaves" at the bottom.
The tree decomposition of a graph is far from unique; for example, a trivial tree decomposition contains all vertices of the graph in its single root node. A tree decomposition in which the underlying tree is a path graph is called a path decomposition, and the width parameter derived from these special types of tree decompositions is known as ...
In computer science, tree traversal (also known as tree search and walking the tree) is a form of graph traversal and refers to the process of visiting (e.g. retrieving, updating, or deleting) each node in a tree data structure, exactly once. Such traversals are classified by the order in which the nodes are visited.
The original tree is converted to a binary tree: each node with more than two children is replaced by a sub-tree in which each node has exactly two children. Each region representing a node (starting from the root) is divided to two, using a line that keeps the angles between edges as large as possible.
Attack tree, conceptual diagrams showing how a target might be attacked; Fault tree diagram, diagram used in deductive failure analysis in various industries; Program structure tree, hierarchical diagram that displays the organization of a computer program; Treemapping, a method for displaying hierarchical data using nested figures, usually ...
The left tree is the decision tree we obtain from using information gain to split the nodes and the right tree is what we obtain from using the phi function to split the nodes. The resulting tree from using information gain to split the nodes. Now assume the classification results from both trees are given using a confusion matrix.
The set of all nodes at a given depth is sometimes called a level of the tree. The root node is at depth zero. Height - Length of the path from the root to the deepest node in the tree. A (rooted) tree with only one node (the root) has a height of zero. In the example diagram, the tree has height of 2. Sibling - Nodes that share the same parent ...