Categorical Features
28 🏗️ 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
- 79 Correlated Overview
- 80 High Correlation Filter
Outliers
Imbalanced Data
Miscellaneous
Spatial
Time-Series Data
Image Data
Ralational Data
Video Data
- 138 Video Overview
- 139 🏗️ Temporary
Sound Data
- 140 Sound Overview
- 141 🏗️ Temporary
142 🏗️ Order of transformations
143 🏗️ What should you do if you have sparse data?
144 🏗️ How Different Models Deal With Input
145 🏗️ Summary
References

Table of contents

28.1 Weight of Evidence Encoding
28.2 Pros and Cons
- 28.2.1 Pros
- 28.2.2 Cons
28.3 R Examples
28.4 Python Examples

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Categorical Features
28 🏗️ Weight of Evidence Encoding

28 🏗️ Weight of Evidence Encoding

28.1 Weight of Evidence Encoding

28.2 Pros and Cons

28.2.1 Pros

28.2.2 Cons

28.3 R Examples

28.4 Python Examples

27 🏗️ Catboost Encoding

29 🏗️ James-Stein Encoding' class='pagination-link' href='/categorical-jamesstein'> 29 🏗️ James-Stein Encoding

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