Download or read book Classification and Regression Trees written by Leo Breiman and published by Routledge. This book was released on 2017-10-19 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Classification and Regression Trees written by Leo Breiman and published by Routledge. This book was released on 2017-10-19 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Tree Structured Classification Via Generalized Discriminant Analysis written by Wei-Yin Loh and published by . This book was released on 1986 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear techniques are used recursively to construct classification rules which can be represented as k-nary decision trees. The method has been implemented in a computer program called FACT. It can handle ordered and unordered variables, unequal priors, variable misclassification costs, and missing observations. Besides the tree structure, it also yields an importance ranking of the variables and a cross-validation estimate of error. FACT is compared with CART (a procedure proposed recently by Breiman et al., which gives a binary tree) in a series of examples. The conclusion is that FACT and CART are usually comparable in terms of classification accuracy and interpretative capability, but FACT runs many times faster. (Author).
Download or read book Tree structured Classification written by Yu-Shan Shih and published by . This book was released on 1993 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Tree structured Classification for Multivariate Binary Responses written by Jiuzhou Wang and published by . This book was released on 2002 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: hit rate, recursive partitioning, multivariate binary response, QSAR, classification tree.
Download or read book Probability written by Leo Breiman and published by SIAM. This book was released on 1968-01-01 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Tree structured Classification Via Recursive Discriminant Analysis written by Nunta Vanichsetakul and published by . This book was released on 1986 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Tree structured Classification for Multivariate Binary Responses written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, a new algorithm of tree-structured classification for multivariate binary responses, the majority-vote method, is proposed. The majority-vote method is a variation of the original work of Breiman et al (1984) on Classification And Regression Trees. The majority-vote method is similar to CART in that both methods use node impurity as the basis of the splitting rules. The majority-vote method differs from CART in that it determines tree size by choosing an optimal threshold value so that the cross-validated hit rate is maximized, whereas CART uses cost-complexity pruning to determine the optimal tree size. The original motivation of this work is to handle incomplete data, missing and censoring, in a Quantitative Structure Activity Relationship (QSAR) context, where the responses are continuous measurements of activity levels. We proceed by discretizing the responses into binary variables and using the majority-vote method to analyze the resulting binary responses. The performance of the majority-vote method is compared to its continuous response counterpart, MultiSCAM, a tree-structured algorithm for analyzing multivariate continuous responses. Multivariate analysis of variance (MANOVA) is used to evaluate the relative information loss due to discretization. The predictivity of the majority-vote method is evaluated by hit rate, a commonly used criterion in drug discovery. Simulation studies show that the majority-vote method outperforms MultiSCAM for censored data in that it yields higher hit rates.
Download or read book Classification and Regression Trees written by Leo Breiman and published by Wadsworth Publishing Company. This book was released on 1984 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Springer Handbook of Engineering Statistics written by Hoang Pham and published by Springer Nature. This book was released on 2023-04-20 with total page 1136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today’s global and highly competitive environment, continuous improvement in the processes and products of any field of engineering is essential for survival. This book gathers together the full range of statistical techniques required by engineers from all fields. It will assist them to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved. The handbook will be essential reading for all engineers and engineering-connected managers who are serious about keeping their methods and products at the cutting edge of quality and competitiveness.
Download or read book Flexible Imputation of Missing Data Second Edition written by Stef van Buuren and published by CRC Press. This book was released on 2018-07-17 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Discrete Data Analysis with R written by Michael Friendly and published by CRC Press. This book was released on 2015-12-16 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Applied Treatment of Modern Graphical Methods for Analyzing Categorical DataDiscrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data. It explains how to use graphical meth
Download or read book Classification Based on Tree Structured Allocation Rules written by Brandon Vaughn and published by . This book was released on 2005 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider the problem of classifying an unknown observation into one of several populations using tree-structured allocation rules. Although many parametric classification procedures are robust to certain assumption violations, there is need for discriminant procedures that can be utilized regardless of the group-conditional distributions that underline the model. The tree-structured allocation rule will be discussed. Finally, Monte Carlo results are reported to observe the performance of the rule in comparison to a discriminant and logistic regression analysis.
Download or read book Data Mining and Knowledge Discovery Handbook written by Oded Maimon and published by Springer Science & Business Media. This book was released on 2006-05-28 with total page 1378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Download or read book Hands On Machine Learning with R written by Brad Boehmke and published by CRC Press. This book was released on 2019-11-07 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
Download or read book Tree Structure based Hybrid Computational Intelligence written by Yuehui Chen and published by Springer Science & Business Media. This book was released on 2009-11-27 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in computational intelligence is directed toward building thinking machines and improving our understanding of intelligence. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. In this book, the authors illustrate an hybrid computational intelligence framework and it applications for various problem solving tasks. Based on tree-structure based encoding and the specific function operators, the models can be flexibly constructed and evolved by using simple computational intelligence techniques. The main idea behind this model is the flexible neural tree, which is very adaptive, accurate and efficient. Based on the pre-defined instruction/operator sets, a flexible neural tree model can be created and evolved. This volume comprises of 6 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques and data mining will find the comprehensive coverage of this book invaluable.
