Our CPU model only came with six features. Often, we encounter real-world datasets that have a very large number of features arising from a diverse array of measurements. Alternatively, we may have to come up with a large number of features when we aren't really sure what features will be important in influencing our output variable. Moreover, we might have categorical variables with many possible levels from which we are forced to create a large number of new indicator variables, as we saw in Chapter 1, Gearing Up for Predictive Modeling. When our scenario involves a large number of features, we often find that our output only depends on a subset of these. Given k input features, there are 2k distinct subsets that we can form, so for even a moderate number of features the space of subsets is too large for us to fully explore by fitting a model on each subset.
Mastering Predictive Analytics with R - Second Edition
By :
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
Free Chapter
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