Image Data
131 🏗️ Resizing

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

131.1 Resizing
131.2 Pros and Cons
- 131.2.1 Pros
- 131.2.2 Cons
131.3 R Examples
131.4 Python Examples

Edit this page
Report an issue
View source

Image Data
131 🏗️ Resizing

131 🏗️ Resizing

131.1 Resizing

WIP

131.2 Pros and Cons

131.2.1 Pros

131.2.2 Cons

131.3 R Examples

131.4 Python Examples

130 🏗️ Value Normalization

132 🏗️ Changing Brightness

Edit this page
Report an issue
View source