tailieunhanh - Chapter 5: Decision trees

Chapter 5: Decision trees Introduction to decision tree; Decision tree for pattern recognition; Construction of decision trees; Splitting at the nodes; Overfitting and pruning; Example of decision tree induction. | Chapter 5 Decision trees Assoc. Prof. Dr. Duong Tuan Anh Faculty of Computer Science and Engineering, HCMC Univ. of Technology 3/2015 Outline Introduction to decision tree Decision tree for pattern recognition Construction of decision trees Splitting at the nodes Overfitting and pruning Example of decision tree induction 1. Introduction A decision tree is a tree where each non-leaf node is associated with a decision and the leaf nodes are associated with an outcome or class label. Each internal node test one or more attribute values leading to two or more branches. Each branch in turn is associated with a possible value of the decision. These branches are mutually distinct and collective exhaustive. Decision trees are excellent tools for choosing between several courses of action. They provide a highly effective structure with which you can lay out options and investigate the possible outcome of choosing these options. In the case of binary decision tree, there are two outgoing . | Chapter 5 Decision trees Assoc. Prof. Dr. Duong Tuan Anh Faculty of Computer Science and Engineering, HCMC Univ. of Technology 3/2015 Outline Introduction to decision tree Decision tree for pattern recognition Construction of decision trees Splitting at the nodes Overfitting and pruning Example of decision tree induction 1. Introduction A decision tree is a tree where each non-leaf node is associated with a decision and the leaf nodes are associated with an outcome or class label. Each internal node test one or more attribute values leading to two or more branches. Each branch in turn is associated with a possible value of the decision. These branches are mutually distinct and collective exhaustive. Decision trees are excellent tools for choosing between several courses of action. They provide a highly effective structure with which you can lay out options and investigate the possible outcome of choosing these options. In the case of binary decision tree, there are two outgoing edges from the nodes. One edge represent the outcome “yes” or “true” and the other edge, the outcome “no” or “false”. Decision tree classifier: inductive learning 2. Decision Trees for Pattern Recognition Patterns can be classified using decision trees where the nodes in the tree represent the status of the problem after making some decision. The leaf nodes give the class label of the classification rule corresponding to the path from root node to the leaf node. Example: Consider employee records in a company as in the following table: Name Age Qualification Position ----------------------------------------------------- Ram 55 B. Com. Manager Shyam 30 B. Eng. Manager Mohan 40 . Manager Manager No. of Assistants Mood Output ---------------------------------------------------------- Shyam 3 No Medium Shyam 5 No Medium Shyam 1 Yes High Ram 1 Yes Low Ram 5 No Low Ram 5 Yes Low Mohan 1 No Low Mohan 3 Yes Medium Mohan 5 No High Figure Decision tree for pattern classification .

• Cơ sở dữ liệu
• Decision trees
• Introduction to decision tree
• Decision tree for pattern recognition
• Construction of decision trees
• Splitting at the nodes
• Overfitting and pruning
• Fingerprint classification
• Scale invariant feature transform
• Bag of visual words
• Random forest of oblique decision trees
• Oblique decision trees
• Introduction to Artificial Intelligence CS440 ECE448
• Learning Decision Trees
• ID tree consistent with the data
• Another consistent ID tree
• Decision Tree Learning Algorithm
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• Discrete Mathematics I
• Bài giảng Toán rời rạc
• Binary search trees
• Prim’s algorithm
• Kruskal’s algorithm
• Association Rules
• Disease Prediction
• DEFINITIONS
• Search Constraints
• Medical Data
• Using Decision Trees
• Construct a Practical Parser
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• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• POS Disambiguation
• Unknown Word Guessing
• scientific reports
• model language
• process natural language
• International Journal of Computer Networks and Communications Security
• Decision trees and attributional rules in incremental learning
• An intrusion detection model
• Attributional rules classifiers
• Real time intrusion detection model
• Information sciences
• PART without the ‘partial’
• Comprehensible classifiers
• Interpretable models
• Partial decision trees
• Decision tree
• Appraisal tree
• Fuzzy set
• Decision making
• Public sector
• Data Mining
• Alternative Techniques
• training data
• Simple models
• Logistic regression
• Thalassemia carrier screening
• Artificial neural networks
• HbE carrier screening
• Beta thalassemia
• Breast cancer
• Artificial neural network
• Microarray databases
• BMC Musculoskeletal Disorders
• X rays
• Clinical decision making
• Quality improvement
• Bone density
• Decision support systems
• Data warehouse
• Regression trees
• Quality business decision making
• tài liệu báo cáo nghiên cứu khoa học
• cách trình bày báo cáo
• kiến thức toán học
• báo cáo toán học
• công trình toán học
• AISI 304 Stainless
• Immersed in Mixtures
• Fungal Suspensions
• Biotechnological Aspects
• Utilisation of Fusel
• Biolubricant Production
• Lecture Managerial economics
• Managerial economics
• Risk analysis
• Risk aversion
• Probability Concepts