Classification And Regression Trees PDF Download
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Author | : Leo Breiman |
Publisher | : Routledge |
Total Pages | : 370 |
Release | : 2017-10-19 |
Genre | : Mathematics |
ISBN | : 135146048X |
Download Classification and Regression Trees Book in PDF, ePub and Kindle
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Author | : Leo Breiman |
Publisher | : Routledge |
Total Pages | : 368 |
Release | : 2017-10-19 |
Genre | : Mathematics |
ISBN | : 1351460498 |
Download Classification and Regression Trees Book in PDF, ePub and Kindle
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Author | : Kirill Dubovikov |
Publisher | : Packt Publishing Ltd |
Total Pages | : 276 |
Release | : 2019-11-12 |
Genre | : Computers |
ISBN | : 1838824561 |
Download Managing Data Science Book in PDF, ePub and Kindle
Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.
Author | : Stef van Buuren |
Publisher | : CRC Press |
Total Pages | : 444 |
Release | : 2018-07-17 |
Genre | : Mathematics |
ISBN | : 0429960352 |
Download Flexible Imputation of Missing Data, Second Edition Book in PDF, ePub and Kindle
Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.
Author | : Leo Breiman |
Publisher | : SIAM |
Total Pages | : 421 |
Release | : 1968-01-01 |
Genre | : Mathematics |
ISBN | : 9781611971286 |
Download Probability Book in PDF, ePub and Kindle
Well known for the clear, inductive nature of its exposition, this reprint volume is an excellent introduction to mathematical probability theory. It may be used as a graduate-level text in one- or two-semester courses in probability for students who are familiar with basic measure theory, or as a supplement in courses in stochastic processes or mathematical statistics. Designed around the needs of the student, this book achieves readability and clarity by giving the most important results in each area while not dwelling on any one subject. Each new idea or concept is introduced from an intuitive, common-sense point of view. Students are helped to understand why things work, instead of being given a dry theorem-proof regime.
Author | : Christoph Molnar |
Publisher | : Lulu.com |
Total Pages | : 320 |
Release | : 2020 |
Genre | : Artificial intelligence |
ISBN | : 0244768528 |
Download Interpretable Machine Learning Book in PDF, ePub and Kindle
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Author | : Oded Z Maimon |
Publisher | : World Scientific |
Total Pages | : 328 |
Release | : 2014-09-03 |
Genre | : Computers |
ISBN | : 9814590096 |
Download Data Mining With Decision Trees: Theory And Applications (2nd Edition) Book in PDF, ePub and Kindle
Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.This book invites readers to explore the many benefits in data mining that decision trees offer:
Author | : Yisehac Yohannes |
Publisher | : Intl Food Policy Res Inst |
Total Pages | : 59 |
Release | : 1999-01-01 |
Genre | : Technology & Engineering |
ISBN | : 0896293378 |
Download Classification and Regression Trees, CART Book in PDF, ePub and Kindle
Author | : Clinton Sheppard |
Publisher | : Createspace Independent Publishing Platform |
Total Pages | : 152 |
Release | : 2017-09-09 |
Genre | : Decision trees |
ISBN | : 9781975860974 |
Download Tree-based Machine Learning Algorithms Book in PDF, ePub and Kindle
"Learn how to use decision trees and random forests for classification and regression, their respective limitations, and how the algorithms that build them work. Each chapter introduces a new data concern and then walks you through modifying the code, thus building the engine just-in-time. Along the way you will gain experience making decision trees and random forests work for you."--Back cover.
Author | : Chris Albon |
Publisher | : "O'Reilly Media, Inc." |
Total Pages | : 305 |
Release | : 2018-03-09 |
Genre | : Computers |
ISBN | : 1491989335 |
Download Machine Learning with Python Cookbook Book in PDF, ePub and Kindle
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models