TSK - Chapter 4 - Classification

Classification is the task of assigning objects to one of several pre-defined categories. E.g - detecting spam email messages based upon the message header and content, categorizing cells as malignant or benign based upon the results of MRI scans, and classifying galaxies based upon their shapes ..etc

• 4. Classification
• 4.1 Preliminaries
• 4.2 General Approach to solving a classification problem
• 4.3 Decision Tree Induction 
• 4.3.1 How a Decision Tree works
• 4.3.2 How to build a Decision Tree
• 4.3.3 Methods for expressing attribute test conditions
• 4.3.4 Measure for selecting the Best Split
• 4.3.5 Algorithm for Decision Tree induction
• 4.3.6 An Example : Web Robot Detection
• 4.3.7 Characteristics of Decision Tree Induction
• 4.4 Model Overfitting
• 4.4.1 Overfitting due to presence of noise
• 4.4.2 Overfitting due to lack of representative samples
• 4.4.3 Overfitting and the multiple comparison procedure
• 4.4.4 Estimation of Generalization Errors
• 4.4.5 Handling overfitting in Decision Tree Induction
• 4.5 Evaluating the performance of a Classifier
• 4.5.1 Holdout method
• 4.5.2 Random subsampling
• 4.5.3 Cross Validation
• 4.5.4 Bootstrap
• 4.6 Methods for Comparing Classifiers
• Estimating a confidence interval for accuracy
• Comparing the performance of two models
• Comparing the performance of two classifiers