# Assignment on Decision Tree Approch

Topics: Decision tree, Decision theory, Decision tree learning Pages: 4 (895 words) Published: May 12, 2013
Presentation & Assignment On: Decision Tree Approach Submitted to: Md.Torikul Alam Professor Department of Business Administration Asian university of Bangladesh (Motijheel Campus) Submitted by: MD.SHOAIB ID:201121285 FEROZE MAHMUD MOJUMDER ID: 201120558 Semester: Fall Batch : 42nd Submission date: December 07, 2012

Introduction to Decision Analysis • • • The field of decision analysis provides a framework for making important decisions. Decision analysis allows us to select a decision from a set of possible decision alternatives when uncertainties regarding the future exist of a decision criterion... The goal is to optimize the resulting payoff in terms.

Definition of 'Decision Tree' A schematic tree-shaped diagram used to determine a course of action or show a statistical probability. Each branch of the decision tree represents a possible decision or occurrence. The tree structure shows how one choice leads to the next, and the use of branches indicates that each option is mutually exclusive. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data but not decisions; rather the resulting classification tree can be an input for decision making. This page deals with decision trees in data mining. Decision tree advantages: Amongst other data mining methods, decision trees have various advantages:   

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Simple to understand and interpret. People are able to understand decision tree models after a brief explanation. Requires little data preparation. Other techniques often require data normalization, dummy variables need to be created and blank values to be removed. Able to handle both numerical and categorical data. Other techniques are usually specialized in analyzing datasets that have only one type of variable. Ex: relation rules can be used only with nominal variables while neural networks can be used only with numerical variables....

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