PPT-CS B551: Decision Trees Agenda

Decision trees Complexity Learning curves Combatting overfitting Boosting Recap Still in supervised setting with logical attributes Find a representation of CONCEPT in the form . 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).. 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 Arko. Barman. With additions and modifications by Ch. . Eick. COSC 4335 Data Mining. Example of a Decision Tree. categorical. categorical. continuous. class. Refund. MarSt. TaxInc. YES. NO. NO. NO. Yes. Decision Tree. Advantages. Fast and easy to implement, Simple to understand. Modular, Re-usable. Can be learned .  . can be constructed dynamically from observations and actions in game, we will discuss this further in a future topic called ‘Learning’). 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. Sibel Adali, . Sujoy Sikdar. , Lirong Xia. Multi-Issue Voting. { , } . X. . { , }. Wine (. ).  . Main dishes (. ).  . Goal: Cater to people’s preferences. issues. 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. 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. 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 Decision trees MARIO REGIN What is a decision tree? General purpose prediction and classification mechanism Emerged at the same time as the nascent fields of artificial intelligence and statistical computation Instructor: Kris Hauser. http://cs.indiana.edu/~hauserk. 1. Constraint Propagation …. … is the process of determining how the constraints and the possible values of one variable affect the possible values of other variables. 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. . Pablo Aldama, Kristina . Vatcheva. , PhD. School of Mathematical & Statistical Sciences, University of Texas Rio Grande Val. ley. Data mining methods, such as decision trees, have become essential in healthcare for detecting fraud and abuse, physicians finding effective treatments for their patients, and patients receiving more affordable healthcare services (.