This book serves as a comprehensive guide to statistical learning, emphasizing practical applications and theoretical foundations. It covers essential topics such as regression, classification, and resampling methods, making complex concepts accessible to readers with a background in statistics and mathematics. The inclusion of real-world examples and case studies enhances understanding, while accompanying software tools facilitate hands-on learning. Ideal for students and professionals alike, it bridges the gap between statistical theory and practical implementation in data analysis.
This book presents key modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering.
In the past decade, the surge in computation and information technology has generated vast amounts of data across various fields, including medicine, biology, finance, and marketing. This challenge has prompted the development of new statistical tools and the emergence of areas like data mining, machine learning, and bioinformatics. While these tools share common foundations, they often use different terminologies. This book presents key concepts in these domains within a unified framework, focusing on ideas rather than mathematical complexities. It includes numerous examples and colorful graphics, making it a valuable resource for statisticians and anyone interested in data mining in science or industry. The content covers a wide range of topics, from supervised to unsupervised learning, including neural networks, support vector machines, classification trees, and boosting. This new edition introduces several topics absent from the original, such as graphical models, random forests, ensemble methods, least angle regression, non-negative matrix factorization, and spectral clustering. Additionally, it addresses methods for "wide" data scenarios, including multiple testing and false discovery rates.
Embark on an exhilarating exploration of the modern revolution in statistics led by two influential figures in the field. This book delves into groundbreaking methodologies and transformative ideas that have reshaped statistical practices. It highlights the impact of these innovations on various disciplines, making complex concepts accessible and engaging for readers. Through their insights, the authors illuminate the evolving landscape of data analysis and its significance in today's world.
This book describes the important ideas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry.
This engaging text simplifies complex topics in data science, statistics, and machine learning, making them accessible to readers. Enhanced with exercises, it encourages practical application and deeper understanding, ensuring a comprehensive grasp of these essential fields.
Focusing on the challenges posed by big data, this book explores how the sparsity assumption can help extract meaningful patterns from extensive datasets, even when the number of features exceeds observations. It delves into various techniques, including the lasso for linear regression, generalized penalties, and numerical optimization methods. Additionally, it covers statistical inference for lasso models, sparse multivariate analysis, graphical models, and compressed sensing, providing a comprehensive guide to modern data analysis techniques.
This book provides a comprehensive overview of statistical learning techniques, focusing on concepts and applications rather than theoretical complexities. It covers essential topics such as regression, classification, and resampling methods, making it accessible for beginners. Real-world examples and practical exercises enhance understanding, while the inclusion of R programming helps readers implement the methods discussed. Ideal for students and professionals alike, it serves as a valuable resource for those looking to deepen their knowledge in data analysis and machine learning.
