Tree Based Methods For Statistical Learning In R
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Tree Based Methods For Statistical Learning In R
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Author : Brandon M. Greenwell
language : en
Publisher: CRC Press
Release Date : 2022-06-23
Tree Based Methods For Statistical Learning In R written by Brandon M. Greenwell and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-23 with Business & Economics categories.
Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level, which lie at the cutting edge of modern statistical and machine learning methodology. The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example, users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit), and more. The core chapters also end with a detailed section on relevant software in both R and other opensource alternatives (e.g., Python, Spark, and Julia), and example usage on real data sets. While the book mostly uses R, it is meant to be equally accessible and useful to non-R programmers. Consumers of this book will have gained a solid foundation (and appreciation) for tree-based methods and how they can be used to solve practical problems and challenges data scientists often face in applied work. Features: Thorough coverage, from the ground up, of tree-based methods (e.g., CART, conditional inference trees, bagging, boosting, and random forests). A companion website containing additional supplementary material and the code to reproduce every example and figure in the book. A companion R package, called treemisc, which contains several data sets and functions used throughout the book (e.g., there’s an implementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree). Interesting examples that are of practical use; for example, how to construct partial dependence plots from a fitted model in Spark MLlib (using only Spark operations), or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining, or even improving performance.
Tree Based Methods
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Author : Brandon M. Greenwell
language : en
Publisher: Chapman & Hall/CRC Data Science Series
Release Date : 2022
Tree Based Methods written by Brandon M. Greenwell and has been published by Chapman & Hall/CRC Data Science Series this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Decision making categories.
This book provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary.
An Introduction To Statistical Learning
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Author : Gareth James
language : en
Publisher: Springer Nature
Release Date : 2021-07-29
An Introduction To Statistical Learning written by Gareth James and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-29 with Mathematics categories.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
An Introduction To Statistical Learning
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Author : Gareth James
language : en
Publisher: Springer Nature
Release Date : 2023-08-01
An Introduction To Statistical Learning written by Gareth James and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-01 with Mathematics categories.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Tree Based Methods For Statistical Learning In R
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Author : Brandon M. Greenwell
language : en
Publisher: CRC Press
Release Date : 2022-06-23
Tree Based Methods For Statistical Learning In R written by Brandon M. Greenwell and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-23 with Business & Economics categories.
Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level, which lie at the cutting edge of modern statistical and machine learning methodology. The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example, users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit), and more. The core chapters also end with a detailed section on relevant software in both R and other opensource alternatives (e.g., Python, Spark, and Julia), and example usage on real data sets. While the book mostly uses R, it is meant to be equally accessible and useful to non-R programmers. Consumers of this book will have gained a solid foundation (and appreciation) for tree-based methods and how they can be used to solve practical problems and challenges data scientists often face in applied work. Features: Thorough coverage, from the ground up, of tree-based methods (e.g., CART, conditional inference trees, bagging, boosting, and random forests). A companion website containing additional supplementary material and the code to reproduce every example and figure in the book. A companion R package, called treemisc, which contains several data sets and functions used throughout the book (e.g., there’s an implementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree). Interesting examples that are of practical use; for example, how to construct partial dependence plots from a fitted model in Spark MLlib (using only Spark operations), or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining, or even improving performance.
An Introduction To Statistical Learning
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Author : Gareth James
language : en
Publisher: Springer
Release Date : 2023-09-08
An Introduction To Statistical Learning written by Gareth James and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-08 with categories.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Random Forests With R
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Author : Robin Genuer
language : en
Publisher: Springer Nature
Release Date : 2020-09-10
Random Forests With R written by Robin Genuer and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-10 with Mathematics categories.
This book offers an application-oriented guide to random forests: a statistical learning method extensively used in many fields of application, thanks to its excellent predictive performance, but also to its flexibility, which places few restrictions on the nature of the data used. Indeed, random forests can be adapted to both supervised classification problems and regression problems. In addition, they allow us to consider qualitative and quantitative explanatory variables together, without pre-processing. Moreover, they can be used to process standard data for which the number of observations is higher than the number of variables, while also performing very well in the high dimensional case, where the number of variables is quite large in comparison to the number of observations. Consequently, they are now among the preferred methods in the toolbox of statisticians and data scientists. The book is primarily intended for students in academic fields such as statistical education, but also for practitioners in statistics and machine learning. A scientific undergraduate degree is quite sufficient to take full advantage of the concepts, methods, and tools discussed. In terms of computer science skills, little background knowledge is required, though an introduction to the R language is recommended. Random forests are part of the family of tree-based methods; accordingly, after an introductory chapter, Chapter 2 presents CART trees. The next three chapters are devoted to random forests. They focus on their presentation (Chapter 3), on the variable importance tool (Chapter 4), and on the variable selection problem (Chapter 5), respectively. After discussing the concepts and methods, we illustrate their implementation on a running example. Then, various complements are provided before examining additional examples. Throughout the book, each result is given together with the code (in R) that can be used to reproduce it. Thus, the book offers readers essential information and concepts, together with examples and the software tools needed to analyse data using random forests.
Decision Tree Statistical Learning Models An Application To New Customer Scoring
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Author : Macià Comella Barbé
language : en
Publisher:
Release Date : 2020
Decision Tree Statistical Learning Models An Application To New Customer Scoring written by Macià Comella Barbé and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
The aim of this thesis is to explore, understand and apply statistical learning methods based on decision trees, specifically individual decision trees and bagging, random forests and gradient boosting methods. In order to do this, aresearch has been done and the theory behind each one of these methods understood.The main sources of information are thebooks "Introduction to Statistical Learning" and "The Elements of Statistical Learning" by T. Hastie, R. Tibshirani and J. Friedman.Afterwards this theory is put to practice using a real case data set and the R programming language to experiment with models of the mentioned methods. The data used comes from areal case project in which a business wishes to predict whether anew customer will be a good one based only in the information from its three first purchases.The tools used are also presented, consisting in the different R packages and functions used and its tuning parameters. The strategy used in order to obtain representative results that make possible to understand the concepts presented in the theory is explained. As well as how these results have been extracted.The sensitivity analysis has been done with the Minitab v18 software, provided by the Universitat Politècnica de Catalunya for research purposes.Finally the results are analysed. This analysis is divided in three sections.The first one is focused in a sensitivity analysis of parameters. The results show that, with the used dataset, for gradient boosting the tree depth allowed is critical to obtain a good quality of fit and prevent overfitting, andthe number of iterations allowed needs to be correctly alignedwith the learning parameter used. The results for bagging and random forests (merged as one is a particular case of the other) prove the lack of overfittingintrinsic of these modelsand discovers that if the number of variables is high and these are strongly correlated the recommended number of variables to choose at each tree node does not lead to optimum results. An initial hypothesis to guide the analysis of this fact is proposed but it is not inside the scope of the project to analyse and prove this hypothesis. The second section of the analysis consists in selecting the best performing method and apply it to the availabledataset. The gradient boosting method is chosen as the best one due to higher quality of fit obtained and a more consistent selection of variables among all scenarios. The third section compares the results obtained with gradient boosting versus the logistic regression model done by the student P. Casas in his bachelor thesis"New customers' classifier"based on the same dataset. The results show that gradient boosting performs better in terms of prediction in two of the three models created, though the difference is small, and obtains the same quality of fitin the other case. Comparing variable relevancethe most important one is shared among both methods(the total value of the purchase). Other secondary variables are shared and some of them not. Therefore it can be said there is similarity in general terms but gradient boosting and logistic regression are nottotally close between them,as it happens with the decision tree methods used in the project.
Effective Statistical Learning Methods For Actuaries Ii
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Author : Michel Denuit
language : en
Publisher: Springer Nature
Release Date : 2020-11-16
Effective Statistical Learning Methods For Actuaries Ii written by Michel Denuit and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-16 with Business & Economics categories.
This book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It also exhibits the tools which make it possible to assess the predictive performance of tree-based models. Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and numerical illustrations or case studies. All numerical illustrations are performed with the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. In particular, master's students in actuarial sciences and actuaries wishing to update their skills in machine learning will find the book useful. This is the second of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance.
Mastering Machine Learning With R
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Author : Cory Lesmeister
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-10-28
Mastering Machine Learning With R written by Cory Lesmeister and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-28 with Computers categories.
Master machine learning techniques with R to deliver insights for complex projects About This Book Get to grips with the application of Machine Learning methods using an extensive set of R packages Understand the benefits and potential pitfalls of using machine learning methods Implement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML system Who This Book Is For If you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful. What You Will Learn Gain deep insights to learn the applications of machine learning tools to the industry Manipulate data in R efficiently to prepare it for analysis Master the skill of recognizing techniques for effective visualization of data Understand why and how to create test and training data sets for analysis Familiarize yourself with fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Realize why and how to apply unsupervised learning methods In Detail Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data. The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series. The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages. Style and approach This is a book explains complicated concepts with easy to follow theory and real-world, practical applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints.