18
Classification using Trees and Rules
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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
18.1
Elements of trees
18.1.1
Splitting
18.1.2
Growing
18.1.3
Pruning
18.1.4
Missing Data Handling
18.2
Single Trees
18.2.1
CART
18.2.2
C5.0
18.2.3
Conditional Inference Trees
18.2.4
Oblique Trees
18.2.5
Bayesian Trees
18.3
Bagging
18.4
Random Forest
18.5
Bayesian Additive Regression Trees
18.6
Boosting
18.7
Rule-Based Models
18.7.1
C5.0 Rules
18.7.2
RuleFit
Chapter References
18
Classification using Trees and Rules
18.1
Elements of trees
18.1.1
Splitting
18.1.2
Growing
18.1.3
Pruning
18.1.4
Missing Data Handling
18.2
Single Trees
18.2.1
CART
18.2.2
C5.0
18.2.3
Conditional Inference Trees
18.2.4
Oblique Trees
18.2.5
Bayesian Trees
18.3
Bagging
18.4
Random Forest
18.5
Bayesian Additive Regression Trees
18.6
Boosting
18.7
Rule-Based Models
18.7.1
C5.0 Rules
18.7.2
RuleFit
Chapter References
17
Complex Nonlinear Boundaries
19
Class Imbalances