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The Statistics and Machine Learning with R Workshop

You're reading from   The Statistics and Machine Learning with R Workshop Unlock the power of efficient data science modeling with this hands-on guide

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803240305
Length 516 pages
Edition 1st Edition
Languages
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Author (1):
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Liu Peng Liu Peng
Author Profile Icon Liu Peng
Liu Peng
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Table of Contents (20) Chapters Close

Preface 1. Part 1:Statistics Essentials
2. Chapter 1: Getting Started with R FREE CHAPTER 3. Chapter 2: Data Processing with dplyr 4. Chapter 3: Intermediate Data Processing 5. Chapter 4: Data Visualization with ggplot2 6. Chapter 5: Exploratory Data Analysis 7. Chapter 6: Effective Reporting with R Markdown 8. Part 2:Fundamentals of Linear Algebra and Calculus in R
9. Chapter 7: Linear Algebra in R 10. Chapter 8: Intermediate Linear Algebra in R 11. Chapter 9: Calculus in R 12. Part 3:Fundamentals of Mathematical Statistics in R
13. Chapter 10: Probability Basics 14. Chapter 11: Statistical Estimation 15. Chapter 12: Linear Regression in R 16. Chapter 13: Logistic Regression in R 17. Chapter 14: Bayesian Statistics 18. Index 19. Other Books You May Enjoy

Introducing the cross-entropy loss

The binary CEL, also called the log loss, is often used as the cost function in logistic regression. This is the loss that the logistic regression model will attempt to minimize by moving the parameters. This function takes the predicted probabilities and the corresponding targets as the input and outputs a scalar score, indicating the goodness of fit. For a single observation with a target of y i and predicted probability of p i, the loss is calculated as follows:

Q i(y i, p i) = − [ y i logp i + (1 − y i)log(1 − p i)]

Summing up all individual losses gives the total binary CEL:

Q(y, p) =  1 _ N  ∑ i N Q i =  1 _ N  ∑ i=1 N − [ y i logp i + (1 − y i)log(1 − p i)]

The binary CEL function is a suitable choice for binary classification problems...

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