tailieunhanh - Artificial Intelligence - Lecturer 13: Decision Tree Learning

Over-fitting the training data, handling continuous-valued (., real-valued) attributes, choosing appropriate measures for attribute selection, handling training data with missing attribute values, handling attributes with differing costs. | 5 28 2014 Artificial Intelligence For HEDSPI Project Lecturer 13 - Decision Tree Learning Lecturers Thanh Huong Duc Khanh Dr. Hai V. Pham HUST 1 Decision tree Introduction Decision tree DT learning To approximate a discrete-valued target function The target function is represented by a decision tree A DT can be represented interpreted as a set of IF-THEN rules . easy to read and understand Capable of learning disjunctive expressions DT learning is robust to noisy data One of the most widely used methods for inductive inference Successfully applied to a range of real-world applications 2 1 5 28 2014 Example of a DT Which documents are of my interest Interested Uninterested . . sport . . player . . goal . . sport . Interested Interested Uninterested Example of a DT Does a person play tennis No Yes No Yes Outlook Overcast Temperature Hot Humidity High Wind weak Yes Outlook Rain Temperature Miid Humidity High Wind strong No Outlook Sunny Temperature Hot Humidity High Wind strong No 4 2 5 28 2014 Decision tree Representation 1 Each internal node represents an attribute to be tested by instances Each branch from a node corresponds to a possible value of the attribute associated with that node Each leaf node represents a classification . a class label A learned DT classifies an instance by sorting it down the tree from the root to some leaf node The classification associated with the leaf node is used for the instance 5 Decision tree Representation 2 A DT represents a disjunction of conjunctions of constraints on the attribute values of instances Each path from the root to a leaf corresponds to a conjunction of attribute tests The tree itself is a disjunction of these conjunctions Examples Let s consider the two previous example DTs. 6

• Kỹ thuật lập trình
• Artificial Intelligence
• Advanced search methods
• Trí tuệ nhân tạo
• Training data
• Handling continuous valued
• Decision Tree Learning
• Applying artificial intelligence on e learning
• Compare E learning
• Traditional learning
• Learn 1 1 with virtual assistant
• Artificial Neural Networks (ANN) algorithms
• CS 486686 Artificial Intelligence
• Course administration
• Intelligent Assistive Technology
• A brief history of A
• Properties of task environments
• Artificial intelligence in education
• Keynote speaker abstracts
• Human computer interface issues
• Pedagogical agents
• Models of criminal liability of artificial intelligence
• Criminal liability of artificial intelligence
• Science fiction to prospect for criminal law and policy in Vietnam
• Criminal law and policy in Vietnam
• Criminal law and policy
• Lecture Artificial Intelligence
• Computing machinery
• Thinking humanly
• Cognitive Science
• Lecture note Artificial Intelligence
• Performance measure
• Environment types
• Agent program
• Problem solving
• Problem solving agents
• Problem types
• Problem formulation
• Informed search algorithms
• Best first search
• Local search algorithms
• Simulated annealing
• Genetic algorithms
• Constraint Satisfaction Problems
• Backtracking search for CSPs
• Game playing
• Resource limits
• Games of chance
• Logical agents
• Knowledge based agents
• Wumpus world
• First order logic
• Semantics of FOL
• Fun with sentences
• Wumpus world in FOL
• Inference in first order logic
• Generalized Modus Ponens
• Logic programming
• Bayes’ Rule
• Methods for handling uncertainty
• Making decisions under uncertainty
• Bayesian networks
• Parameterized distributions
• Inference in Bayesian networks
• Variable elimination
• Temporal probability models
• Hidden Markov models
• Kalman filters
• Speech recognition
• Speech sounds
• Word pronunciation
• Rational decisions
• Rational preferences
• Multiattribute utilities
• Learning from Observations
• Learning agents
• Inductive learning