Download or read book Introduction to Classifier Performance Analysis with R written by Sutaip L.C. Saw and published by CRC Press. This book was released on 2024-12-03 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Classification problems are common in business, medicine, science, engineering and other sectors of the economy. Data scientists and machine learning professionals solve these problems through the use of classifiers. Choosing one of these data driven classification algorithms for a given problem is a challenging task. An important aspect involved in this task is classifier performance analysis (CPA). Introduction to Classifier Performance Analysis with R provides an introductory account of commonly used CPA techniques for binary and multiclass problems, and use of the R software system to accomplish the analysis. Coverage draws on the extensive literature available on the subject, including descriptive and inferential approaches to CPA. Exercises are included at the end of each chapter to reinforce learning. Key Features: An introduction to binary and multiclass classification problems is provided, including some classifiers based on statistical, machine and ensemble learning. Commonly used techniques for binary and multiclass CPA are covered, some from less well-known but useful points of view. Coverage also includes important topics that have not received much attention in textbook accounts of CPA. Limitations of some commonly used performance measures are highlighted. Coverage includes performance parameters and inferential techniques for them. Also covered are techniques for comparative analysis of competing classifiers. A key contribution involves the use of key R meta-packages like tidyverse and tidymodels for CPA, particularly the very useful yardstick package. This is a useful resource for upper level undergraduate and masters level students in data science, machine learning and related disciplines. Practitioners interested in learning how to use R to evaluate classifier performance can also potentially benefit from the book. The material and references in the book can also serve the needs of researchers in CPA.

Download or read book Data Science for Healthcare written by Sergio Consoli and published by Springer. This book was released on 2019-02-23 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to healthcare. Building on three tutorial-like chapters on data science in healthcare, the following eleven chapters highlight success stories on the application of data science in healthcare, where data science and artificial intelligence technologies have proven to be very promising. This book is primarily intended for data scientists involved in the healthcare or medical sector. By reading this book, they will gain essential insights into the modern data science technologies needed to advance innovation for both healthcare businesses and patients. A basic grasp of data science is recommended in order to fully benefit from this book.

Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Download or read book Model Based Clustering and Classification for Data Science written by Charles Bouveyron and published by Cambridge University Press. This book was released on 2019-07-25 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.

Download or read book Supervised Machine Learning for Text Analysis in R written by Emil Hvitfeldt and published by CRC Press. This book was released on 2021-10-22 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language. Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem. Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more. If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing. This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines. Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches. Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up. These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.

Download or read book Soft Computing for Problem Solving written by Kedar Nath Das and published by Springer Nature. This book was released on 2019-11-27 with total page 980 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume book presents the outcomes of the 8th International Conference on Soft Computing for Problem Solving, SocProS 2018. This conference was a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), and Vellore Institute of Technology (India), and brought together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to select potential future directions. The book highlights the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers on algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It offers a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems that are difficult to solve using traditional methods.

Download or read book Explanatory Model Analysis written by Przemyslaw Biecek and published by CRC Press. This book was released on 2021-02-15 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.

Download or read book Soft Computing Techniques in Vision Science written by Srikanta Patnaik and published by Springer. This book was released on 2012-02-10 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Special Edited Volume is a unique approach towards Computational solution for the upcoming field of study called Vision Science. From a scientific firmament Optics, Ophthalmology, and Optical Science has surpassed an Odyssey of optimizing configurations of Optical systems, Surveillance Cameras and other Nano optical devices with the metaphor of Nano Science and Technology. Still these systems are falling short of its computational aspect to achieve the pinnacle of human vision system. In this edited volume much attention has been given to address the coupling issues Computational Science and Vision Studies. It is a comprehensive collection of research works addressing various related areas of Vision Science like Visual Perception and Visual system, Cognitive Psychology, Neuroscience, Psychophysics and Ophthalmology, linguistic relativity, color vision etc. This issue carries some latest developments in the form of research articles and presentations. The volume is rich of contents with technical tools for convenient experimentation in Vision Science. There are 18 research papers having significance in an array of application areas. The volume claims to be an effective compendium of computing developments like Frequent Pattern Mining, Genetic Algorithm, Gabor Filter, Support Vector Machine, Region Based Mask Filter, 4D stereo camera systems, Principal Component Analysis etc. The detailed analysis of the papers can immensely benefit to the researchers of this domain. It can be an Endeavour in the pursuit of adding value in the existing stock of knowledge in Vision Science.

Download or read book Performance Analysis in Sport written by Miguel-Angel Gomez-Ruano and published by Frontiers Media SA. This book was released on 2020-12-29 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

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 373 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 Energy Research Abstracts written by and published by . This book was released on 1986 with total page 812 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning Algorithms Handbook written by Aman Kharwal and published by . This book was released on 2023-09-15 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Key Features: Clear Explanations of Machine Learning Algorithms: The book offers clear and concise explanations of machine learning algorithms, ensuring that readers of all levels can grasp the concepts effortlessly. Hands-On Approach: Packed with practical examples using Python and code snippets, you'll gain a hands-on understanding of how each algorithm works and learn to implement them in real projects. Comprehensive Coverage: From linear regression and support vector machines to decision trees and neural networks, the book covers a wide array of algorithms, giving you a solid foundation to explore diverse problem domains. Performance Evaluation Methods: Learn how to evaluate the effectiveness of your models, identify areas for improvement, and optimize their performance using industry-standard evaluation techniques. Data Preprocessing Techniques: Discover the critical elements of data preprocessing that lay the groundwork for building robust and accurate machine learning models. Time Series Forecasting: Explore advanced algorithms specifically designed for time series data, a critical component of numerous real-world applications. Appendix for Easy Reference: Access all parameters of commonly used machine learning algorithms in a handy appendix, facilitating efficient model tuning.

Download or read book Advanced Network Technologies and Intelligent Computing written by Isaac Woungang and published by Springer Nature. This book was released on 2023-03-21 with total page 693 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2022, held in Varanasi, India, during December 22–24, 2022. The 68 full papers and 11 short papers included in this book were carefully reviewed and selected from 443 submissions. They were organized in two topical sections as follows: Advanced Network Technologies and Intelligent Computing.

Download or read book Advances in Hyperspectral Image Processing Techniques written by Chein-I Chang and published by John Wiley & Sons. This book was released on 2022-11-09 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Hyperspectral Image Processing Techniques Authoritative and comprehensive resource covering recent hyperspectral imaging techniques from theory to applications Advances in Hyperspectral Image Processing Techniques is derived from recent developments of hyperspectral imaging (HSI) techniques along with new applications in the field, covering many new ideas that have been explored and have led to various new directions in the past few years. The work gathers an array of disparate research into one resource and explores its numerous applications across a wide variety of disciplinary areas. In particular, it includes an introductory chapter on fundamentals of HSI and a chapter on extensive use of HSI techniques in satellite on-orbit and on-board processing to aid readers involved in these specific fields. The book’s content is based on the expertise of invited scholars and is categorized into six parts. Part I provides general theory. Part II presents various Band Selection techniques for Hyperspectral Images. Part III reviews recent developments on Compressive Sensing for Hyperspectral Imaging. Part IV includes Fusion of Hyperspectral Images. Part V covers Hyperspectral Data Unmixing. Part VI offers different views on Hyperspectral Image Classification. Specific sample topics covered in Advances in Hyperspectral Image Processing Techniques include: Two fundamental principles of hyperspectral imaging Constrained band selection for hyperspectral imaging and class information-based band selection for hyperspectral image classification Restricted entropy and spectrum properties for hyperspectral imaging and endmember finding in compressively sensed band domain Hyperspectral and LIDAR data fusion, fusion of band selection methods for hyperspectral imaging, and fusion using multi-dimensional information Advances in spectral unmixing of hyperspectral data and fully constrained least squares linear spectral mixture analysis Sparse representation-based hyperspectral image classification; collaborative hyperspectral image classification; class-feature weighted hyperspectral image classification; target detection approach to hyperspectral image classification With many applications beyond traditional remote sensing, ranging from defense and intelligence, to agriculture, to forestry, to environmental monitoring, to food safety and inspection, to medical imaging, Advances in Hyperspectral Image Processing Techniques is an essential resource on the topic for industry professionals, researchers, academics, and graduate students working in the field.

Download or read book Geocomputation with R written by Robin Lovelace and published by CRC Press. This book was released on 2019-03-22 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/.

Download or read book XXVI Brazilian Congress on Biomedical Engineering written by Rodrigo Costa-Felix and published by Springer. This book was released on 2019-05-15 with total page 841 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents the proceedings of the Brazilian Congress on Biomedical Engineering (CBEB 2018). The conference was organised by the Brazilian Society on Biomedical Engineering (SBEB) and held in Armação de Buzios, Rio de Janeiro, Brazil from 21-25 October, 2018. Topics of the proceedings include these 11 tracks: • Bioengineering • Biomaterials, Tissue Engineering and Artificial Organs • Biomechanics and Rehabilitation • Biomedical Devices and Instrumentation • Biomedical Robotics, Assistive Technologies and Health Informatics • Clinical Engineering and Health Technology Assessment • Metrology, Standardization, Testing and Quality in Health • Biomedical Signal and Image Processing • Neural Engineering • Special Topics • Systems and Technologies for Therapy and Diagnosis

Download or read book Machine Learning for Business Analytics written by Galit Shmueli and published by John Wiley & Sons. This book was released on 2023-03-22 with total page 693 pages. Available in PDF, EPUB and Kindle. Book excerpt: MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using R An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.