Categorical Features
29 🏗️ James-Stein 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

29.1 James-Stein 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 🏗️ James-Stein Encoding

29 🏗️ James-Stein Encoding

29.1 James-Stein Encoding

29.2 Pros and Cons

29.2.1 Pros

29.2.2 Cons

29.3 R Examples

29.4 Python Examples

28 🏗️ Weight of Evidence Encoding

30 🏗️ M-Estimator Encoding' class='pagination-link' href='/categorical-mestimator'> 30 🏗️ M-Estimator Encoding

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