Motivation | Placeholders for Parameters | Specifying Arguments | Process

Introduction | Example

Defining candidate ensemble members | Putting together a stack

Define candidate ensemble members | Putting together a stack | Fit the stack

Creating Dummy Variables | Interactions with Dummy Variables | Warning! | Getting All of the Indicator Variables | Novel Levels | Other Steps Related to Dummy Variables

An Example | An Initial Recipe | Preprocessing Steps | Checks

Example: Class Imbalance Sampling and Skipping Steps | Other Examples | How To Skip Steps | Be Careful!

The Formula Method | The Non-Formula Interface | Role Inheritance

Model Trees | Ensembles By Committees | Instance-Based Corrections | Model tuning | Extracting Rules | Variable Importance | Exporting the Model Using the RuleQuest file format | Current Limitations

Intro | Example setup | Model inside the database | Categorical variables | Multiple linear regression | Interactions | Full example

Intro | Example setup | Running Kmeans clustering | Under the hood | Safeguards for long running jobs

Recap

Transforming a variable | Handling levels in categorical data | Dummy variables | Recommended preprocessing outline

Classification Trees | Rule-Based Models | Predictions | Cost-Sensitive Models

Introduction | Basic Desirability Functions | Overall Desirability | An Example | Maximizing Desirability | Non–Standard Features | References