A Pack of Statistical Wolves: Bootstrap, and Statistical Machine Learning, Boosting, Bagging, Stacking
Key Features
- Implement machine learning algorithms to build ensemble-efficient models
- Explore powerful R packages to create predictive models, using ensemble methods
- Learn to build ensemble models on large datasets using a practical approach
Book Description
Ensemble Techniques- the technique of combining two or more similar or dissimilar machine leaning algorithms to create a strong model that delivers superior prediction power-can give your datasets a boost in accuracy.
In this book, you begin with the important statistical bootstrap and model averaging methods and then go the distance in terms of learning the central trilogy of ensemble techniques: bagging, random forest, and boosting. We explain the three most powerful types of ensemblers in R-boosting, bagging, and stacking-and how they can be used to provide better accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms that can be used to build ensemble models. Later you will also explore how to improve the performance for your ensemble models.
By the end of this book you will understand how machine learning algorithms can be combined to reduce common problems, and build simple efficient machine learning models with real world examples.
What you will learn
- Essential review of resampling methods, bootstrap, and model averaging
- Detailed coverage of the ensemble methods: bagging, random forests, and boosting
- Use multiple algorithms to make strong predictive models
- Comprehensive treatment of boosting methods
- Supplement methods with statistical tests such as the ROC test
- Treatment of four statistical and machine learning data structures in classification, regression, survival, and time series
- Use the supplied R code to implement ensemble methods
Who This Book Is For
This book is for data scientists, machine learning developers who want to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R is expected to get the most out of the book.
Prabhanjan Narayanachar Tattar is a lead statistician and manager at the Global Data Insights & Analytics division of Ford Motor Company, Chennai. He received the IBS(IR)-GK Shukla Young Biometrician Award (2005) and Dr. U.S. Nair Award for Young Statistician (2007). He held SRF of CSIR-UGC during his PhD. He has authored books such as Statistical Application Development with R and Python, 2nd Edition, Packt; Practical Data Science Cookbook, 2nd Edition, Packt; and A Course in Statistics with R, Wiley. He has created many R packages.
