Too Many Variables
77 🏗️ Filter based feature selection

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

77.1 Filter based feature selection
77.2 Pros and Cons
- 77.2.1 Pros
- 77.2.2 Cons
77.3 R Examples
77.4 Python Examples

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Too Many Variables
77 🏗️ Filter based feature selection

77 🏗️ Filter based feature selection

77.1 Filter based feature selection

WIP

77.2 Pros and Cons

77.2.1 Pros

77.2.2 Cons

77.3 R Examples

77.4 Python Examples

76 🏗️ ISOMAP

78 🏗️ Wrapper based feature selection

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