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. Battiti. , Mauro . Brunato. .. The LION Way: Machine Learning . plus.  Intelligent Optimization. .. LIONlab. , University of Trento, Italy, . Apr 2015. http://intelligent-optimization.org/LIONbook. 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. 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 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. 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. 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 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 . by Holly Nguyen, Hongyu Pan, Lei Shi, Muhammad Tahir. Showcasing work by . Themis P. Exarchos, Alexandros T. Tzallas, DinaBaga, Dimitra Chaloglou, Dimitrios I. Fotiadis, Sofia Tsouli, Maria Diako. u. 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.