12.2.1 Selecting Features without Overfitting.

11.2.1 Simple Filters Applied to the Parkinson’s Disease Data.

11.1 Illustrative Data: Predicting Parkinson’s Disease.

10.4 Overfitting to Predictors and External Validation.

10.2 Classes of Feature Selection Methodologies.

9.6 Impacts of Data Processing on Modeling.

9.2 What are the Experimental Unit and the Unit of Prediction?.

9.1 Illustrative Data: Pharmaceutical Manufacturing Monitoring.

8.2 Models that are Resistant to Missing Values.

8.1 Understanding the Nature and Severity of Missing Information.

7.4.1 Guiding Principles and Two-stage Modeling.

7.4 Approaches when Complete Enumeration is Practically Impossible.7.3 The Brute-Force Approach to Identifying Predictive Interactions.7.1 Guiding Principles in the Search for Interactions.6.2.2 Discretize Predictors as a Last Resort.6.2.1 Nonlinear Features via Basis Expansions and Splines.5.7 Factors versus Dummy Variables in Tree-Based Models.5.2 Encoding Predictors with Many Categories.5.1 Creating Dummy Variables for Unordered Categories.4.4 Post Modeling Exploratory Visualizations.4.3.2 Exploring Relationships Between Categorical Predictors.4.3.1 Visualizing Relationships between Outcomes and Predictors.

4.3 Visualizations for Categorical Data: Exploring the OkCupid Data.

4.2.2 Augmenting Visualizations through Faceting, Colors, and Shapes.

4.2.1 Box Plots, Violin Plots, and Histograms.

4.2 Visualizations for Numeric Data: Exploring Train Ridership Data.

4.1 Introduction to the Chicago Train Ridership Data.

3.8 Feature Engineering Without Overfitting.

3.7 Comparing Models Using the Training Set.

3.4.7 What Should Be Included Inside of Resampling?.

3.4.1 V-Fold Cross-Validation and Its Variants.

3.1 Illustrative Example: OkCupid Profile Data.

3 A Review of the Predictive Modeling Process.

2 Illustrative Example: Predicting Risk of Ischemic Stroke.

1.2.6 Experience-Driven Modeling and Empirically Driven Modeling.

1.2.4 The Model versus the Modeling Process.

1.2.2 Supervised and Unsupervised Procedures.

What does low and high variance mean