Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
Bayesian Analysis with Python

You're reading from   Bayesian Analysis with Python Unleash the power and flexibility of the Bayesian framework

Arrow left icon
Product type Paperback
Published in Nov 2016
Last Updated in Feb 2025
Publisher Packt
ISBN-13 9781785883804
Length 282 pages
Edition 1st Edition
Languages
Arrow right icon
Toc

Table of Contents (10) Chapters Close

Preface 1. Thinking Probabilistically - A Bayesian Inference Primer 2. Programming Probabilistically – A PyMC3 Primer FREE CHAPTER 3. Juggling with Multi-Parametric and Hierarchical Models 4. Understanding and Predicting Data with Linear Regression Models 5. Classifying Outcomes with Logistic Regression 6. Model Comparison 7. Mixture Models 8. Gaussian Processes Index

Logistic regression

My mother prepares a delicious dish called sopa seca, which is basically a spaghetti-based recipe and which translates literally from Spanish as dry soup. While it may sound like a misnomer or even an oxymoron, the name of the dish makes more sense when we learn how it is cooked. Something similar happens with the logistic regression, a model that despite its name is used to solve classification problems rather than regression ones. The logistic regression model is an extension of the linear regression models we saw in the previous chapter, and thus its name. To understand how we can use a regression model to classify, let us began by rewriting the core of the linear model but this time including a small twist as follows:

Logistic regression

Where, f is some function known to us as the inverse link function. Why do we call f the inverse link function instead of just the link function? The reason is that traditionally people thought about these kinds of functions as functions linking the...

lock icon The rest of the chapter is locked
Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Bayesian Analysis with Python
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Modal Close icon
Modal Close icon