Book Image

Regression Analysis with R

By : Giuseppe Ciaburro
Book Image

Regression Analysis with R

By: Giuseppe Ciaburro

Overview of this book

Regression analysis is a statistical process which enables prediction of relationships between variables. The predictions are based on the casual effect of one variable upon another. Regression techniques for modeling and analyzing are employed on large set of data in order to reveal hidden relationship among the variables. This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. The first few chapters give an understanding of what the different types of learning are – supervised and unsupervised, how these learnings differ from each other. We then move to covering the supervised learning in details covering the various aspects of regression analysis. The outline of chapters are arranged in a way that gives a feel of all the steps covered in a data science process – loading the training dataset, handling missing values, EDA on the dataset, transformations and feature engineering, model building, assessing the model fitting and performance, and finally making predictions on unseen datasets. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. The practical examples are illustrated using R code including the different packages in R such as R Stats, Caret and so on. Each chapter is a mix of theory and practical examples. By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects.
Table of Contents (15 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Summary


In this final chapter, we have explored multiple linear regression, logistic regression, random forest regression, and neural network techniques applied to datasets resulting from real cases. We started from a random forest regression for the Boston dataset to predict the median value of owner-occupied homes for the test data. The random forests algorithm is based on the construction of many regression trees. Every single case is passed through all the trees in the forest; each of them provides a prediction. The final forecast is then made by averaging the predictions provided by individual regression trees. In accordance with what has been said, the tree response is an estimate of the dependent variable given the predictors.

Then, we have used a logistic regression technique to classify breast cancer. Logistic regression is a special case of a generalized linear model having as a link function the logit function. This is a regression model applied in cases where the dependent variable...