| |
| |
| Preface | |
| |
| |
| |
| Dimension Reduction Methods | |
| |
| |
| Need for Dimension Reduction in Data Mining | |
| |
| |
| Principal Components Analysis | |
| |
| |
| Factor Analysis | |
| |
| |
| User-Defined Composites | |
| |
| |
| |
| Regression Modeling | |
| |
| |
| Example of Simple Linear Regression | |
| |
| |
| Least-Squares Estimates | |
| |
| |
| Coefficient or Determination | |
| |
| |
| Correlation Coefficient | |
| |
| |
| The ANOVA Table | |
| |
| |
| Outliers, High Leverage Points, and Influential Observations | |
| |
| |
| The Regression Model | |
| |
| |
| Inference in Regression | |
| |
| |
| Verifying the Regression Assumptions | |
| |
| |
| An Example: The Baseball Data Set | |
| |
| |
| An Example: The California Data Set | |
| |
| |
| Transformations to Achieve Linearity | |
| |
| |
| |
| Multiple Regression and Model Building | |
| |
| |
| An Example of Multiple Regression | |
| |
| |
| The Multiple Regression Model | |
| |
| |
| Inference in Multiple Regression | |
| |
| |
| Regression with Categorical Predictors | |
| |
| |
| Multicollinearity | |
| |
| |
| Variable Selection Methods | |
| |
| |
| An Application of Variable Selection Methods | |
| |
| |
| Mallows' C p Statistic | |
| |
| |
| Variable Selection Criteria | |
| |
| |
| Using the Principal Components as Predictors in Multiple Regression | |
| |
| |
| |
| Logistic Regression | |
| |
| |
| A Simple Example of Logistic Regression | |
| |
| |
| Maximum Likelihood Estimation | |
| |
| |
| Interpreting Logistic Regression Output | |
| |
| |
| Inference: Are the Predictors Significant? | |
| |
| |
| Interpreting the Logistic Regression Model | |
| |
| |
| Interpreting a Logistic Regression Model for a Dichotomous Predictor | |
| |
| |
| Interpreting a Logistic Regression Model for a Polychotomous Predictor | |
| |
| |
| Interpreting a Logistic Regression Model for a Continuous Predictor | |
| |
| |
| The Assumption of Linearity. The Zero-Cell Problem | |
| |
| |
| Multiple Logistic Regression | |
| |
| |
| Introducing Higher Order terms to Handle Non-Linearity | |
| |
| |
| Validating the Logistic Regression Model | |
| |
| |
| WEKA: Hands-On Analysis Using Logistic Regression | |
| |
| |
| |
| Na�ve Bayes and Bayesian Networks | |
| |
| |
| The Bayesian Approach | |
| |
| |
| The Maximum a Posteriori (MAP) Classification | |
| |
| |
| The Posterior Odds Ratio | |
| |
| |
| Balancing the Data | |
| |
| |
| Na�ve Bayes Classification | |
| |
| |
| Numeric Predictors for Na�ve Bayes Classification | |
| |
| |
| WEKA: Hands-On Analysis Using Na�ve Bayes | |
| |
| |
| Bayesian Belief Networks | |
| |
| |
| Using the Bayesian Network to Find Probabilities | |
| |
| |
| WEKA: Hands-On Analysis Using Bayes Net | |
| |
| |
| |
| Genetic Algorithms | |
| |
| |
| Introduction to Genetic Algorithms | |
| |
| |
| The Basic Framework of a Genetic Algorithm | |
| |
| |
| A Simple Example of Genetic Algorithms at Work | |
| |
| |
| Modifications and Enhancements: Selection | |
| |
| |
| Modifications and enhancements: Crossover | |
| |
| |
| Genetic Algorithms for Real-Valued Variables | |
| |
| |
| Using Genetic Algorithms to Train a Neural Network | |
| |
| |
| WEKA: Hands-On Analysis Using Genetic Algorithms | |
| |
| |
| |
| Case Study: Modeling Response to Direct-Mail Marketing | |
| |
| |
| The Cross-Industry Standard Process for Data Mining: CRISP-DM. Business Understanding Phase | |
| |
| |
| Data Understanding and Data Preparation Phases | |
| |
| |
| The Modeling Phase and the Evaluation Phase | |
| |
| |
| Index | |