19
Classification Ensembles
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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
Classification Ensembles
20
Class Imbalances
21
Classification Case Study
22
Classification Summary
Regression
Characterization
Finalization
Table of contents
19.1
Bagging
19.2
Random Forest
19.3
Bayesian Additive Regression Trees
19.4
Boosting
19.5
Rule-Based Ensembles
19.5.1
C5.0 Rules
19.5.2
RuleFit
Chapter References
19
Classification Ensembles
19.1
Bagging
19.2
Random Forest
19.3
Bayesian Additive Regression Trees
19.4
Boosting
19.5
Rule-Based Ensembles
19.5.1
C5.0 Rules
19.5.2
RuleFit
Chapter References
18
Classification using Trees and Rules
20
Class Imbalances