Too Many Variables
73 🏗️ Generalized Discriminant Analysis

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

73.1 Generalized Discriminant Analysis
73.2 Pros and Cons
- 73.2.1 Pros
- 73.2.2 Cons
73.3 R Examples
73.4 Python Examples

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Too Many Variables
73 🏗️ Generalized Discriminant Analysis

73 🏗️ Generalized Discriminant Analysis

73.1 Generalized Discriminant Analysis

WIP

73.2 Pros and Cons

73.2.1 Pros

73.2.2 Cons

73.3 R Examples

73.4 Python Examples

72 🏗️ Linear Discriminant Analysis

74 🏗️ Autoencoders

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