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Tree-Based Methods for Statistical Learning in R

This book provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I;

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Effective Statistical Learning Methods for Actuaries II

This book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It;

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Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods

architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical;

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Introduction to Machine Learning with R

learning algorithms for supervised and unsupervised cases Examine statistical concepts for designing data for use in models Dive into linear;

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An Introduction to Statistical Learning

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for;

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An Introduction to Statistical Learning

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for;

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Rank-Based Methods for Shrinkage and Selection

Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based;

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Nonparametric Statistical Methods Using R

A Practical Guide to Implementing Nonparametric and Rank-Based Procedures Nonparametric Statistical Methods Using R covers traditional;

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Nonparametric Statistical Methods Using R

gives an overview of the R language and basic statistical concepts before discussing nonparametrics. It presents rank-based methods for one- and;

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Handbook of Regression Analysis With Applications in R

Handbook and reference guide for students and practitioners of statistical regression-based analyses in R Handbook of Regression;

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Beginning R 4

for big data), and new data import settings for text (as well as the statistical methods to model text-based, categorical data). Each;

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Supervised Machine Learning

interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework;

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Supervised Machine Learning

interoperability with R libraries to accomplish variety of statistical and machine-learning tasks Computer programs in R and SAS that create AI framework;

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Statistical Methods for Field and Laboratory Studies in Behavioral Ecology

Statistical Methods for Field and Laboratory Studies in Behavioral Ecology focuses on how statistical methods may be used to make sense;

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Statistical Methods for Field and Laboratory Studies in Behavioral Ecology

Statistical Methods for Field and Laboratory Studies in Behavioral Ecology focuses on how statistical methods may be used to make sense;

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Person-Centered Methods

covariates, moderator CFA, and CFA modeling branches in tree-based methods. The new developments enable the use of categorical together with;

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Understanding and Applying Basic Statistical Methods Using R

Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and;

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R Through Excel: A Spreadsheet Interface for Statistics, Data Analysis, and Graphics

In this book, the authors build on RExcel, a free add-in for Excel that can be downloaded from the R distribution network. RExcel;

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Statistics and Machine Learning Methods for EHR Data

The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning;

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Machine Learning for Knowledge Discovery with R

Machine Learning for Knowledge Discovery with R contains methodologies and examples for statistical modelling, inference, and prediction;

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Statistical Modelling in Higher Learning

Statistical Modelling in Higher Learning: The book explains the factors that predict the time taken to complete degree programmes.The book;

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Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare

of computer science and statistics. While much of machine-learning is statistics-based, achievements in deep learning for image and language;

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Statistical Computing with R

statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to;

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Mastering Machine Learning with R

book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful. What You Will;

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Introdu To Statistical Computing

for exploring statistical models. This book gives a comprehensive introduction to the exciting area of sampling-based methods. An Introduction;

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JAMA Guide to Statistics and Methods

any online entitlements included with the product. The world-renowned experts at JAMA (R) explain statistical analysis and the methods;

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Biostatistics with R: An Introduction to Statistics Through Biological Data

Biostatistics with R is designed around the dynamic interplay among statistical methods, their applications in biology, and their;

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