Categorical Features
32 🏗️ Quantile 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

32.1 Quantile Encoding
32.2 Pros and Cons
- 32.2.1 Pros
- 32.2.2 Cons
32.3 R Examples
32.4 Python Examples

Edit this page
Report an issue
View source

Categorical Features
32 🏗️ Quantile Encoding

32 🏗️ Quantile Encoding

32.1 Quantile Encoding

32.2 Pros and Cons

32.2.1 Pros

32.2.2 Cons

32.3 R Examples

32.4 Python Examples

31 🏗️ Thermometer Encoding

33 🏗️ Summary Encoding' class='pagination-link' href='/categorical-summary'> 33 🏗️ Summary Encoding

Edit this page
Report an issue
View source