Bagging and boosting are two techniques used to combine learners. These techniques are classified under the generic name of ensembles (or meta-algorithm) because the ultimate goal is actually to ensemble weak learners to create a more sophisticated, but more accurate, model. There is no formal definition of a weak learner, but ideally it's a fast, sometimes linear model that not necessarily produces excellent results (it suffices that they are just better than a random guess). The final ensemble is typically a non-linear learner whose performance increases with the number of weak learners in the model (note that the relation is strictly non-linear). Let's now see how they work.
Regression Analysis with Python
By :
Regression Analysis with Python
By:
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.
Related Content you might be interested in
Current Title:
Regression Analysis with Python
No titles found
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