17
Complex Nonlinear Boundaries
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Preface
News
Contributing
Introduction
1
Introduction
2
The Whole Game
Preparation
3
Initial Data Splitting
4
Missing Data
5
Transforming Numeric Predictors
6
Working with Categorical Predictors
7
Embeddings
8
Interactions and Nonlinear Features
Optimization
9
Overfitting
10
Measuring Performance with Resampling
11
Grid Search
12
Iterative Search
13
Feature Selection
14
Comparing Models
Classification
15
Characterizing Classification Models
16
Generalized Linear and Additive Classifiers
17
Complex Nonlinear Boundaries
18
Classification using Trees and Rules
19
Class Imbalances
20
Classification Case Study
21
Classification Summary
Regression
Characterization
Finalization
Table of contents
17.1
Nonlinear Discriminants
17.1.1
Quadratic and Regularized Approaches
17.1.2
Mixture Discriminants
17.1.3
Flexible Discriminants
17.1.4
Naive Bayes
17.2
Neural networks
17.2.1
Single layer, Feedforward Networks
17.2.2
Tabular Network Models
17.3
K-Nearest Neighbors
17.3.1
Discriminant Adaptive Nearest Neighbors
17.4
Support Vector Machines
Chapter References
17
Complex Nonlinear Boundaries
17.1
Nonlinear Discriminants
17.1.1
Quadratic and Regularized Approaches
17.1.2
Mixture Discriminants
17.1.3
Flexible Discriminants
17.1.4
Naive Bayes
17.2
Neural networks
17.2.1
Single layer, Feedforward Networks
17.2.2
Tabular Network Models
17.3
K-Nearest Neighbors
17.3.1
Discriminant Adaptive Nearest Neighbors
17.4
Support Vector Machines
Chapter References
16
Generalized Linear and Additive Classifiers
18
Classification using Trees and Rules