Too Many Variables
72 🏗️ Linear 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

72.1 Linear Discriminant Analysis
72.2 Pros and Cons
- 72.2.1 Pros
- 72.2.2 Cons
72.3 R Examples
72.4 Python Examples

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Too Many Variables
72 🏗️ Linear Discriminant Analysis

72 🏗️ Linear Discriminant Analysis

72.1 Linear Discriminant Analysis

WIP

72.2 Pros and Cons

72.2.1 Pros

72.2.2 Cons

72.3 R Examples

72.4 Python Examples

71 🏗️ Non-Negative Matrix Factorization

73 🏗️ Generalized Discriminant Analysis

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