Categorical Features
33 🏗️ Summary 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

33.1 Summary Encoding
33.2 Pros and Cons
- 33.2.1 Pros
- 33.2.2 Cons
33.3 R Examples
33.4 Python Examples

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Categorical Features
33 🏗️ Summary Encoding

33 🏗️ Summary Encoding

33.1 Summary Encoding

33.2 Pros and Cons

33.2.1 Pros

33.2.2 Cons

33.3 R Examples

33.4 Python Examples

32 🏗️ Quantile Encoding

34 Collapsing Categories' class='pagination-link' href='/categorical-collapse'> 34 Collapsing Categories

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