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A decision tree is a decision support recursive partitioning structure that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
Value tree analysis is a multi-criteria decision-making (MCDM) implement by which the decision-making attributes for each choice to come out with a preference for the decision makes are weighted. [1] Usually, choices' attribute-specific values are aggregated into a complete method. Decision analysts (DAs) distinguished two types of utility. [2]
An influence diagram (ID) (also called a relevance diagram, decision diagram or a decision network) is a compact graphical and mathematical representation of a decision situation. It is a generalization of a Bayesian network , in which not only probabilistic inference problems but also decision making problems (following the maximum expected ...
The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. [34] [35] Consequently, practical decision-tree learning algorithms are based on heuristics such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms cannot ...
Information gain (decision tree) Information gain ratio; L. Logistic model tree; R. Random forest
Decision Tree Model. In computational complexity theory, the decision tree model is the model of computation in which an algorithm can be considered to be a decision tree, i.e. a sequence of queries or tests that are done adaptively, so the outcome of previous tests can influence the tests performed next.
An issue tree showing how a company can increase profitability: A profitability tree is an example of an issue tree. It looks at different ways in which a company can increase its profitability. Starting from the key question on the left, it breaks it down between revenues and costs, and break these down into further details.
The left figure below shows a binary decision tree (the reduction rules are not applied), and a truth table, each representing the function (,,).In the tree on the left, the value of the function can be determined for a given variable assignment by following a path down the graph to a terminal.