Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts
R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems.
This book is designed to be both a guide and a reference for moving beyond the basics of predictive modeling. The book begins with a dedicated chapter on the language of models and the predictive modeling process. 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 data sets.
By the end of this book, you will have explored and tested the most popular modeling techniques in use on real world data sets and mastered a diverse range of techniques in predictive analytics.
What You Will Learn
Master the steps involved in the predictive modeling process
Learn how to classify predictive models and distinguish which models are suitable for a particular problem
Understand how and why each predictive model works
Recognize the assumptions, strengths, and weaknesses of a predictive model, and that there is no best model for every problem
Select appropriate metrics to assess the performance of different types of predictive model
Diagnose performance and accuracy problems when they arise and learn how to deal with them
Grow your expertise in using R and its diverse range of packages
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