After having confirmed the values of the coefficients of the linear model we have built, and after having explored the basic statistics necessary to understand if our model is working correctly, we can start auditing our work by first understanding how a prediction is made up. We can obtain this by accounting for each variable's role in the constitution of the predicted values. A first check to be done on the coefficients is surely on the directionality they express, which is simply dictated by their sign. Based on our expertise on the subject (so it is advisable to be knowledgeable about the domain we are working on), we can check whether all the coefficients correspond to our expectations in terms of directionality. Some features may decrease the response as we expect, thereby correctly confirming that they have a coefficient with a negative sign, whereas others may increase it, so a positive coefficient should be correct. When coefficients do not correspond...
Regression Analysis with Python
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Regression Analysis with Python
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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.
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Table of Contents (16 chapters)
Regression Analysis with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Regression – The Workhorse of Data Science
Approaching Simple Linear Regression
Multiple Regression in Action
Logistic Regression
Data Preparation
Achieving Generalization
Online and Batch Learning
Advanced Regression Methods
Real-world Applications for Regression Models
Index
Customer Reviews