PPT-Decision Trees in R

Arko Barman Slightly edited by Ch Eick COSC 6335 Data Mining Decision Trees Used for classifying data by partitioning attribute space Tries to find axisparallel decision boundaries for specified optimality criteria. 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?. Classify as positive if K out of 30 trees Classify as positive if K out of 30 trees predict positive. Vary K.predict positive. Vary K. Generating ROC CurvesGenerating ROC Curves Linear Threshold Uni Decision Trees. Gavin Brown. www.cs.man.ac.uk/~gbrown. Recap: threshold classifiers. height. weight. Q. Where is a good threshold?. 10 20 30 40 50 60. 1. 0. Also known as “decision stump”. From Decision . 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. 2. Decision Trees and Decision Tables. Often our problem solutions require decisions to be made according to two or more conditions or combinations of conditions. Decision trees represent such decision as a sequence of steps. 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. A . tree. is a connected undirected graph with no simple circuits.. Since a tree cannot have a simple circuit, a tree cannot contain multiple edges or loops.. Therefore, any tree must be a . simple graph. Copyright © Andrew W. Moore. Density Estimation – looking ahead. Compare it against the two other major kinds of models:. Regressor. Prediction of. real-valued output. Input. Attributes. Density. Estimator. Chapter 5 Divide and Conquer – Classification Using Decision Trees and Rules decision trees and rule learners two machine learning methods that make complex decisions from sets of simple choices Statin Choice Decision Aid Share-Decision Making SCIP Shared Decision Making Shared Decision Making Glasziou and Haynes ACP JC 2005 Promote a process where patients and clinicians make a choice together. 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 Decision Trees and Decision Tables 2 Decision Trees and Decision Tables Often our problem solutions require decisions to be made according to two or more conditions or combinations of conditions Decision trees represent such decision as a 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. .