Essay on Decision Tree : Decision Trees

Essay on Decision Tree : Decision Trees

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Decision Trees
David Studdard
January 1, 2016
Cognitive Software Design
Dr. Steve
South University












The objectives and main findings of this paper is, the overview of decision trees. Decision tree techniques have been used to build models that are closely compared to the human reasoning and that are easily understood (Kotsiantis, 2013). A decision tree is shaped like a tree to determine a course of action or a probability of a statistic and each branch will represent a decision or an occurrence. If you have a complex problem a decision tree can be used to clarify and find an answer. Over the years many decision tree algorithms have been developed including C4.5 (Kotsiantis, 2013). There have been studies done that show that C4.5 has both a combination of error rate and speed. Also this paper will cover basic issues, splitting measures, multivariate splits, controlling decision tree complexity, pruning, feature selection, decision tree characteristics, handling special problems with decision trees, handling very large datasets with decision trees, handling cost-sensitive problems with decision trees, handling concept drifts with decision trees, handling uncertain data with decision trees, handling ordinal classifications with decision trees, handling multi-instance data with decision trees, hybrid methods, fuzzy decision tree algorithms, and ensembles of decision trees (Kotsiantis, 2013).
There are two major procedures which a decision tree is made, the building (induction), and the classification (inference). You start the building process by starting with an empty tree and selecting the right node for the test attribute using the attribute selection measure. The idea is to use the attribute that will reduce the amount...


... middle of paper ...


...he information either cases or certain components from being considered by the search algorithm.
• Making an interpretation of the tree structure into another information structure, for example, an arrangement (Kotsiantis, 2013).
As mentioned earlier there are weaknesses of decision trees, and they are, increased storage, increased computation, all classifiers must be processed, and decreased comprehensibility, with involvement of multiple classifiers in decision-making, it is more difficult for non-expert users to perceive the underlying reasoning process leading to a decision (Kotsiantis, 2013).






References:
Kotsiantis, S. B. (2013). Decision trees: A recent overview. The Artificial Intelligence Review, 39(4), 261–283. Retrieved from http://www.thecampuscommon.com/library/ezproxy/ticketdemocs.asp?sch=auo&turl=http://search.proquest.com/docview/1317666743

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