Resampling Methods for Variable Selection and Classification PDF Download
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Author: Yevgeniya Jane M. Fridlyand
Publisher:
ISBN:
Category :
Languages : en
Pages : 372
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Book Description
Author: Yevgeniya Jane M. Fridlyand
Publisher:
ISBN:
Category :
Languages : en
Pages : 372
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Book Description
Author: Max Kuhn
Publisher: CRC Press
ISBN: 1351609467
Category : Business & Economics
Languages : en
Pages : 266
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Book Description
The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Author: Stef van Buuren
Publisher: CRC Press
ISBN: 0429960352
Category : Mathematics
Languages : en
Pages : 444
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Book Description
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: P. Karuppusamy
Publisher: Springer Nature
ISBN: 9811925410
Category : Technology & Engineering
Languages : en
Pages : 730
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Book Description
This book features a collection of high-quality, peer-reviewed papers presented at the Second International Conference on Ubiquitous Intelligent Systems (ICUIS 2022) organized by Shree Venkateshwara Hi-Tech Engineering College, Tamil Nadu, India, during March 10–11, 2022. The book covers topics such as cloud computing, mobile computing and networks, embedded computing frameworks, modeling and analysis of ubiquitous information systems, communication networking models, big data models and applications, ubiquitous information processing systems, next-generation ubiquitous networks and protocols, advanced intelligent systems, Internet of Things, wireless communication and storage networks, intelligent information retrieval techniques, AI-based intelligent information visualization techniques, cognitive informatics, smart automation systems, health care informatics and bioinformatics models, security and privacy of intelligent information systems, and smart distributed information systems.
Author: D. S. Guru
Publisher: Springer Nature
ISBN: 3031224051
Category : Computers
Languages : en
Pages : 436
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Book Description
This volume constitutes the refereed proceedings of the Eighth International Conference on Cognition and Recognition, ICCR 2021, held in Mandya, India, in December 2021. The 24 full papers and 9 short papers presented were carefully reviewed and selected from 150 submissions. The ICCR conference aims to bring together leading academic Scientists, Researchers and Research scholars to exchange and share their experiences and research results on all aspects of Computer Vision, Image Processing Machine Learning and Deep Learning Technologies.
Author: A. C. Davison
Publisher: Cambridge University Press
ISBN: 9780521574716
Category : Computers
Languages : en
Pages : 606
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Book Description
Disk contains the library functions and documentation for use with Splus for Windows.
Author: Gennady Agre
Publisher: Springer
ISBN: 3319993445
Category : Computers
Languages : en
Pages : 297
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Book Description
This book constitutes the refereed proceedings of the 18th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2018, held in Varna, Bulgaria, in September 2018. The 22 revised full papers and 7 poster papers presented were carefully reviewed and selected from 72 submissions. They cover a wide range of topics in AI: from machine learning to natural language systems, from information extraction to text mining, from knowledge representation to soft computing; from theoretical issues to real-world applications.
Author: Carolin Strobl
Publisher: Cuvillier Verlag
ISBN: 3867276617
Category :
Languages : en
Pages : 203
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Book Description
Author:
Publisher: Elsevier
ISBN: 0444640452
Category : Science
Languages : en
Pages : 732
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Book Description
Data Analysis for Omic Sciences: Methods and Applications, Volume 82, shows how these types of challenging datasets can be analyzed. Examples of applications in real environmental, clinical and food analysis cases help readers disseminate these approaches. Chapters of note include an Introduction to Data Analysis Relevance in the Omics Era, Omics Experimental Design and Data Acquisition, Microarrays Data, Analysis of High-Throughput RNA Sequencing Data, Analysis of High-Throughput DNA Bisulfite Sequencing Data, Data Quality Assessment in Untargeted LC-MS Metabolomic, Data Normalization and Scaling, Metabolomics Data Preprocessing, and more. - Presents the best reference book for omics data analysis - Provides a review of the latest trends in transcriptomics and metabolomics data analysis tools - Includes examples of applications in research fields, such as environmental, biomedical and food analysis
Author: Brad Boehmke
Publisher: CRC Press
ISBN: 1000730433
Category : Business & Economics
Languages : en
Pages : 373
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Book Description
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.
