Book Image

Regression Analysis with Python

By : Luca Massaron, Alberto Boschetti
4 (1)
Book Image

Regression Analysis with Python

4 (1)
By: Luca Massaron, Alberto Boschetti

Overview of this book

Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. There are many kinds of regression algorithms, and the aim of this book is to explain which is the right one to use for each set of problems and how to prepare real-world data for it. With this book you will learn to define a simple regression problem and evaluate its performance. The book will help you understand how to properly parse a dataset, clean it, and create an output matrix optimally built for regression. You will begin with a simple regression algorithm to solve some data science problems and then progress to more complex algorithms. The book will enable you to use regression models to predict outcomes and take critical business decisions. Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer.
Table of Contents (16 chapters)
Regression Analysis with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Multiclass Logistic Regression


The extension to Logistic Regression, for classifying more than two classes, is Multiclass Logistic Regression. Its foundation is actually a generic approach: it doesn't just work for Logistic Regressors, it also works with other binary classifiers. The base algorithm is named One-vs-rest, or One-vs-all, and it's simple to grasp and apply.

Let's describe it with an example: we have to classify three kinds of flowers and, given some features, the possible outputs are three classes: f1, f2, and f3. That's not what we've seen so far; in fact, this is not a binary classification problem. Instead, it seems very easy to break down this problem into three simpler problems:

  • Problem #1: Positive examples (that is, the ones that get the label "1") are f1; negative examples are all the others

  • Problem #2: Positive examples are f2; negative examples are f1 and f3

  • Problem #3: Positive examples are f3; negative examples are f1 and f2

For all three problems, we can use a binary...