You're reading from Python Feature Engineering Cookbook A complete guide to crafting powerful features for your machine learning models

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835883587

Length 396 pages

Edition 3rd Edition

Languages

Python

Tools

Combine

Concepts

Data Science

Author (1):

Soledad Galli

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Table of Contents (14) Chapters

Preface

1. Chapter 1: Imputing Missing Data

2. Chapter 2: Encoding Categorical Variables FREE CHAPTER

3. Chapter 3: Transforming Numerical Variables

4. Chapter 4: Performing Variable Discretization

5. Chapter 5: Working with Outliers

6. Chapter 6: Extracting Features from Date and Time Variables

7. Chapter 7: Performing Feature Scaling

8. Chapter 8: Creating New Features

9. Chapter 9: Extracting Features from Relational Data with Featuretools

10. Chapter 10: Creating Features from a Time Series with tsfresh

11. Chapter 11: Extracting Features from Text Variables

12. Index

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13. Other Books You May Enjoy

Encoding with Weight of Evidence

Weight of Evidence (WoE) was developed primarily for credit and financial industries to facilitate variable screening and exploratory analysis and to build more predictive linear models to evaluate the risk of loan defaults.

The WoE is computed from the basic odds ratio:

$<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mrow><mrow><mi>WoE</mi><mo>=</mo><mi>log</mi><mrow><mrow><mo>(</mo><mfrac><mrow><mi>p</mi><mi>r</mi><mi>o</mi><mi>p</mi><mi>o</mi><mi>r</mi><mi>t</mi><mi>i</mi><mi>o</mi><mi>n</mi><mi>p</mi><mi>o</mi><mi>s</mi><mi>i</mi><mi>t</mi><mi>i</mi><mi>v</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>s</mi><mi>e</mi><mi>s</mi></mrow><mrow><mi>p</mi><mi>r</mi><mi>o</mi><mi>p</mi><mi>o</mi><mi>r</mi><mi>t</mi><mi>i</mi><mi>o</mi><mi>n</mi><mi>n</mi><mi>e</mi><mi>g</mi><mi>a</mi><mi>t</mi><mi>i</mi><mi>v</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>s</mi><mi>e</mi><mi>s</mi></mrow></mfrac><mo>)</mo></mrow></mrow></mrow></mrow></math>$

Here, positive and negative refer to the values of the target being 1 or 0, respectively. The proportion of positive cases per category is determined as the sum of positive cases per category group divided by the total positive cases in the training set. The proportion of negative cases per category is determined as the sum of negative cases per category group divided by the total number of negative observations in the training set.

WoE has the following characteristics:

WoE = 0 if p(positive) / p(negative) = 1; that is, if the outcome is random
WoE > 0 if p(positive) > p(negative)
WoE < 0 if p(negative) > p(positive)

This allows us to...

You're reading from Python Feature Engineering Cookbook A complete guide to crafting powerful features for your machine learning models

Table of Contents (14) Chapters

Encoding with Weight of Evidence

Authors (1)

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