Categorical Features
29 🏗️ Weight of Evidence Encoding

Feature Engineering A-Z

Preface
Introduction
Numeric Features
Categorical Features
Datetime Features
Missing Data
Text Features
Circular Features
Too Many Variables
Correlated Data
- 80 Correlated Overview
- 81 High Correlation Filter
Outliers
Imbalanced Data
Miscellaneous
Spatial
Time-Series Data
Image Data
Ralational Data
Video Data
- 139 Video Overview
- 140 🏗️ Temporary
Sound Data
- 141 Sound Overview
- 142 🏗️ Temporary
143 🏗️ Order of transformations
144 🏗️ What should you do if you have sparse data?
145 🏗️ How Different Models Deal With Input
146 🏗️ Summary
References

Table of contents

29.1 Weight of Evidence Encoding
29.2 Pros and Cons
- 29.2.1 Pros
- 29.2.2 Cons
29.3 R Examples
29.4 Python Examples

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Categorical Features
29 🏗️ Weight of Evidence Encoding

29 🏗️ Weight of Evidence Encoding

29.1 Weight of Evidence Encoding

29.2 Pros and Cons

29.2.1 Pros

29.2.2 Cons

29.3 R Examples

29.4 Python Examples

28 🏗️ Catboost Encoding

30 🏗️ James-Stein Encoding

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