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The GQM [9] approach provides a method for defining goals, refining them into questions and finally data to be collected, and then analyzing and interpreting them. Several instruments and tools are available for visualizing the GQM model (e.g., as an abstraction sheet or a GQM tree). Balanced Scorecard (BSC) [10] links strategic objectives and ...
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 ...
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.
Fast-and-frugal tree or matching heuristic [1] (in the study of decision-making) is a simple graphical structure that categorizes objects by asking one question at a time. These decision trees are used in a range of fields: psychology , artificial intelligence , and management science .
Subsequently, Kaplan and David P. Norton included anonymous details of this balanced scorecard design in a 1992 article. [5] Although Kaplan and Norton's article was not the only paper on the topic published in early 1992, [10] it was a popular success, and was quickly followed by a second in 1993. [11]
One of the questions that arises in a decision tree algorithm is the optimal size of the final tree. A tree that is too large risks overfitting the training data and poorly generalizing to new samples. A small tree might not capture important structural information about the sample space.
The first diagrams of this type appeared in the early 1990s, and the idea of using this type of diagram to help document Balanced Scorecard was discussed in a paper by Robert S. Kaplan and David P. Norton in 1996. [1] The strategy map idea featured in several books and articles during the late 1990s by Robert S. Kaplan and David P. Norton.
The feature with the optimal split i.e., the highest value of information gain at a node of a decision tree is used as the feature for splitting the node. The concept of information gain function falls under the C4.5 algorithm for generating the decision trees and selecting the optimal split for a decision tree node. [1] Some of its advantages ...