Too Many Variables
80 🏗️ Embedded based feature selection

Feature Engineering A-Z

Preface
Introduction
Numeric Features
Categorical Features
Datetime Features
Missing Data
Text Features
Periodic Features
Too Many Variables
Correlated Data
- 81 Correlated Overview
- 82 High Correlation Filter
Outliers
Imbalanced Data
Miscellaneous
Spatial
Time-Series Data
Image Data
Ralational Data
Video Data
- 140 Video Overview
- 141 🏗️ Temporary
Sound Data
- 142 Sound Overview
- 143 🏗️ Temporary
144 🏗️ Order of transformations
145 🏗️ What should you do if you have sparse data?
146 🏗️ How Different Models Deal With Input
147 🏗️ Summary
References

Table of contents

80.1 Embedded based feature selection
80.2 Pros and Cons
- 80.2.1 Pros
- 80.2.2 Cons
80.3 R Examples
80.4 Python Examples

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Too Many Variables
80 🏗️ Embedded based feature selection

80 🏗️ Embedded based feature selection

80.1 Embedded based feature selection

WIP

80.2 Pros and Cons

80.2.1 Pros

80.2.2 Cons

80.3 R Examples

80.4 Python Examples

79 🏗️ Wrapper based feature selection

81 Correlated Overview

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