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Front Cover
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C4.5: Programs for Machine Learning
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Copyright Page
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Table of Contents
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Preface
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Obtaining the C4.5 Code
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CHAPTER 1. Introduction
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1.1 Example: Labor negotiation settlements
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1.2 Other kinds of classification models
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1.3 What lies ahead
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CHAPTER 2. Constructing Decision Trees
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2.1 Divide and conquer
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2.2 Evaluating tests
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2.3 Possible tests considered
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2.4 Tests on continuous attributes
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CHAPTER 3. Unknown Attribute Values
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3.1 Adapting the previous algorithms
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3.2 Play/Don't Play example again
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3.3 Recapitulation
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CHAPTER 4. Pruning Decision Trees
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4.1 When to simplify?
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4.2 Error-based pruning
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4.3 Example: Democrats and Republicans
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4.4 Estimating error rates for trees
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CHAPTER 5. From Trees to Rules
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5.1 Generalizing single rules
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5.2 Class rulesets
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5.3 Ranking classes and choosing a default
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5.4 Summary
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CHAPTER 6. Windowing
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6.1 Example: Hypothyroid conditions revisited
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6.2 Why retain windowing?
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6.3 Example: The multiplexor
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CHAPTER 7. Grouping Attribute Values
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7.1 Finding value groups by merging
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7.2 Example: Soybean diseases
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7.3 When to form groups?
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7.4 Example: The Monk's problems
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7.5 Uneasy reflections
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CHAPTER 8. Interacting with Classification Models
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8.1 Decision tree models
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8.2 Production rule models
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8.3 Caveat
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CHAPTER 9. Guide to Using the System
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9.1 Files
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9.2 Running the programs
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9.3 Conducting experiments
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9.4 Using options: A credit approval example
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CHAPTER 10. Limitations
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10.1 Geometric interpretation
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10.2 Nonrectangular regions
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10.3 Poorly delineated regions
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10.4 Fragmented regions
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10.5 A more cheerful note
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CHAPTER 11. Desirable Additions
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11.1 Continuous classes
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11.2 Ordered discrete attributes
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11.3 Structured attributes
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11.4 Structured induction
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11.5 Incremental induction
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11.6 Prospectus
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Appendix: Program Listings
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Brief descriptions of the contents of the files
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Notes on some important data structures
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File Makefile
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Alphabetic index of routines
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References and Bibliography
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Author Index
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Subject Index
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