PPT-Decision Trees

ID Hair Height Weight Lotion Result Sarah Blonde Average Light No Sunburn Dana Blonde Tall Average Yes none Alex Brown Tall Average Yes None Annie Blonde Short Average No. 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 Arko. . Barman. Slightly edited by Ch. . Eick. COSC 6335 Data Mining. Decision Trees. Used for classifying data by partitioning attribute space. Tries to find axis-parallel 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?. 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’). 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. 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 . Sibel Adali, . Sujoy Sikdar. , Lirong Xia. Multi-Issue Voting. { , } . X. . { , }. Wine (. ).  . Main dishes (. ).  . Goal: Cater to people’s preferences. issues. 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. 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 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 Lecture 15: Decision Trees Outline Motivation Decision Trees Splitting criteria Stopping Conditions & Pruning Text Reading: Section 8.1, p. 303-314. 2 Geometry of Data Recall: l ogistic regression 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. .