Image Data
127 🏗️ Color Modifications

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

127.1 Color Modifications
127.2 Pros and Cons
- 127.2.1 Pros
- 127.2.2 Cons
127.3 R Examples
127.4 Python Examples

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Image Data
127 🏗️ Color Modifications

127 🏗️ Color Modifications

127.1 Color Modifications

(includes brightness and contrast)

WIP

127.2 Pros and Cons

127.2.1 Pros

127.2.2 Cons

127.3 R Examples

127.4 Python Examples

126 🏗️ Greyscale conversion

128 🏗️ Noise Reduction' class='pagination-link' href='/image-noise'> 128 🏗️ Noise Reduction

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