PPT-Induction of Decision Trees (IDT)

CSE 335435 Resources Main Artificial Intelligence A Modern Approach Russell and Norvig Chapter Learning from Examples Alternatives http wwwdmiunictit apulvirentiagdQui86pdf. Battiti. , Mauro . Brunato. .. The LION Way: Machine Learning . plus.  Intelligent Optimization. .. LIONlab. , University of Trento, Italy, . Apr 2015. http://intelligent-optimization.org/LIONbook. 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 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. Sources:. Chapter 3, Lenz et al Book: Case-based Reasoning Technology. www.aic.nrl.navy.mil/~aha/research/applications.html. Information Gain Formula. Patrons?. none. X7(-),x11(-). some. X1(+),x3(+),x6(+),x8(+). 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. Section 5.3. Section Summary. Recursively Defined Functions. Recursively Defined Sets and Structures. Structural Induction. Generalized Induction. Recursively Defined Functions. . Definition. : A . 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. .