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Decision trees can also be seen as generative models of induction rules from empirical data. An optimal decision tree is then defined as a tree that accounts for most of the data, while minimizing the number of levels (or "questions"). [8] Several algorithms to generate such optimal trees have been devised, such as ID3/4/5, [9] CLS, ASSISTANT ...
An issue tree, also called logic tree, is a graphical breakdown of a question that dissects it into its different components vertically and that progresses into details as it reads to the right. [1]: 47 Issue trees are useful in problem solving to identify the root causes of a problem as well as to identify its potential solutions. They also ...
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 ...
For a contingent planning problem, a plan is no longer a sequence of actions but a decision tree because each step of the plan is represented by a set of states rather than a single perfectly observable state, as in the case of classical planning. [8] The selected actions depend on the state of the system.
The general process for applying an EC to problem solving is described by Cohen (2010) as follows: [8]: 676 Identify the type of problem (there are variations in the way the diagrams are constructed for different types of problems.) Write a storyline of this problem in a factual, objective way, even if the problem causes an emotional upset.
A decision support system (DSS) is an information system that supports business or organizational decision-making activities. DSSs serve the management, operations and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance—i.e., unstructured and semi-structured ...
An upper bound for a decision-tree model was given by Meyer auf der Heide [17] who showed that for every n there exists an O(n 4)-deep linear decision tree that solves the subset-sum problem with n items. Note that this does not imply any upper bound for an algorithm that should solve the problem for any given n.
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.