In the previous chapter, where we talked about the predictive modeling process, we delved into the importance of assessing a trained model's performance using training and test datasets. In this section, we will look at specific measures of performance that we will frequently encounter when describing the predictive accuracy of different models. It turns out that depending on the class of the problem, we will need to use slightly different ways of assessing (the model's) performance. As we focus on supervised models in this book, we will look at how to assess regression models and classification models. For classification models, we will also discuss some additional metrics used for the binary classification task, which is a very important and frequently encountered type of problem.
![Book Image](https://content.packt.com/B06139/cover_image_small.jpg)
Mastering Predictive Analytics with R - Second Edition
By :
![Book Image](https://content.packt.com/B06139/cover_image_small.jpg)
Mastering Predictive Analytics with R - Second Edition
By:
Overview of this book
R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems.
The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do you train models that can handle really large datasets? This book will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks.
By the end of this book, you will have explored and tested the most popular modeling techniques in use on real- world datasets and mastered a diverse range of techniques in predictive analytics using R.
Table of Contents (22 chapters)
Mastering Predictive Analytics with R Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Gearing Up for Predictive Modeling
Tidying Data and Measuring Performance
Linear Regression
Generalized Linear Models
Neural Networks
Support Vector Machines
Tree-Based Methods
Dimensionality Reduction
Ensemble Methods
Probabilistic Graphical Models
Topic Modeling
Recommendation Systems
Scaling Up
Index
Customer Reviews