PPT-Decision Analysis-Decision Trees

A decision tree is a graphical representation of every possible sequence of decision and random outcomes states of nature that can occur within a given decision making problem A decision tree is composed of a collection of nodes represented by circles and squares interconnected by branches represented by lines. T state 8712X action or input 8712U uncertainty or disturbance 8712W dynamics functions XUW8594X w w are independent RVs variation state dependent input space 8712U 8838U is set of allowed actions in state at time brPage 5br Policy action is function trees (cont.). If we pick the adjacent nucleotide, what gene tree do we expect?. A. C. B. A-B coalescence. AB-C coalescence. Split 2. Split 1. If we pick a nucleotide from a distant part of the genome, what gene tree do we expect?. Are . really . cool!. Mangrove Trees. Grow where no other trees can grow!. Have huge roots!. They l. ook . like this!. Mangrove Importance. “If there were no mangrove forests, then the sea will have no meaning. It is like having a tree without roots, because mangroves are the roots of the sea.”. D. D. . Sleator. and R. E. . Tarjan. | AT&T Bell Laboratories. Journal of the ACM . | Volume 32 | Issue 3 | Pages 652-686 | 1985. Presented By: . James A. Fowler, Jr. | November 30, 2010. George Mason University | Fairfax, Virginia. CSE 335/435. Resources:. Main: . Artificial Intelligence: A Modern Approach (Russell and . Norvig. ; Chapter “Learning from Examples. ”). Alternatives:. http. ://www.dmi.unict.it/~. apulvirenti/agd/Qui86.pdf. Alternatives and States of Nature. Good Decisions vs. Good Outcomes. Payoff Matrix. Decision Trees. Utility Functions. Decisions under Uncertainty. Decisions under Risk. Decision Analysis - Payoff Tables. Training Set:. Play Tennis?. Weak. Rain Mild High Weak No. Rain Mild High Weak No. Decision Trees: Another Example. Training Set:. Play Tennis?. Sunny. Weak. Tandy Warnow. Joint work with . Siavash. . Mirarab. , . Md. S. . Bayzid. , and others. Orangutan. Gorilla. Chimpanzee. Human. From the Tree of the Life Website. ,. . University . of . Arizona. Dates from Lock et al. Nature, 2011. Object-based classifiers. Others. DECISION TREES. Non-parametric approach. Data mining tool used in many applications, not just RS. Classifies data by building rules based on image values. Rules form trees that are multi-branched with nodes and “leaves” or endpoints. Dr. Halimah Alshehri. 1. Introduction to Trees. DEFINITION 1 . A . tree. is a connected undirected graph with no simple circuits.. Because . a tree cannot have a simple circuit. , . a tree cannot contain multiple edges or loops. Sultan Almuhammadi ICS 254: Graphs and Trees 1 Graph & Trees Chapters 10-11 Acknowledgement This is a modified version of Module#22 on Graph Theory by Michael Frank Sultan Almuhammadi ICS 254: Graphs and Trees 6. 9. 2. 4. 1. 8. <. >. =. © 2014 Goodrich, Tamassia, Goldwasser. Presentation for use with the textbook . Data Structures and Algorithms in Java, 6. th. edition. , by M. T. Goodrich, R. Tamassia, and M. H. Goldwasser, Wiley, 2014. How is normal Decision Tree different from Random Forest?. A Decision Tree is a supervised learning strategy in machine learning. It may be used with both classification and regression algorithms. . As the name says, it resembles a tree with nodes. The branches are determined by the number of criteria. It separates data into these branches until a threshold unit is reached. . Good Decisions vs. Good Outcomes. Payoff Matrix. Decision Trees. Utility Functions. Decisions under Uncertainty. Decisions under Risk. Decision Analysis - Payoff Tables. Case Problem - (A) p. 38. Decision Analysis - Payoff Tables.