Applied Machine Learning Using Mlr3 In R

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Applied Machine Learning Using mlr3 in R

Author : Bernd Bischl
Publisher : CRC Press
ISBN 13 : 1003830579
Total Pages : 356 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Applied Machine Learning Using mlr3 in R by : Bernd Bischl

Download or read book Applied Machine Learning Using mlr3 in R written by Bernd Bischl and published by CRC Press. This book was released on 2024-01-18 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: mlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. Applied Machine Learning Using mlr3 in R gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R. It covers various key topics, including basic machine learning tasks, such as building and evaluating a predictive model; hyperparameter tuning of machine learning approaches to obtain peak performance; building machine learning pipelines that perform complex operations such as pre-processing followed by modelling followed by aggregation of predictions; and extending the mlr3 ecosystem with custom learners, measures, or pipeline components. Features: In-depth coverage of the mlr3 ecosystem for users and developers Explanation and illustration of basic and advanced machine learning concepts Ready to use code samples that can be adapted by the user for their application Convenient and expressive machine learning pipelining enabling advanced modelling Coverage of topics that are often ignored in other machine learning books The book is primarily aimed at researchers, practitioners, and graduate students who use machine learning or who are interested in using it. It can be used as a textbook for an introductory or advanced machine learning class that uses R, as a reference for people who work with machine learning methods, and in industry for exploratory experiments in machine learning.

Applied Machine Learning Using mlr3 in R

Author : Bernd Bischl
Publisher : CRC Press
ISBN 13 : 1003830587
Total Pages : 385 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Applied Machine Learning Using mlr3 in R by : Bernd Bischl

Download or read book Applied Machine Learning Using mlr3 in R written by Bernd Bischl and published by CRC Press. This book was released on 2024-01-18 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: mlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. Applied Machine Learning Using mlr3 in R gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R. It covers various key topics, including basic machine learning tasks, such as building and evaluating a predictive model; hyperparameter tuning of machine learning approaches to obtain peak performance; building machine learning pipelines that perform complex operations such as pre-processing followed by modelling followed by aggregation of predictions; and extending the mlr3 ecosystem with custom learners, measures, or pipeline components. Features: In-depth coverage of the mlr3 ecosystem for users and developers Explanation and illustration of basic and advanced machine learning concepts Ready to use code samples that can be adapted by the user for their application Convenient and expressive machine learning pipelining enabling advanced modelling Coverage of topics that are often ignored in other machine learning books The book is primarily aimed at researchers, practitioners, and graduate students who use machine learning or who are interested in using it. It can be used as a textbook for an introductory or advanced machine learning class that uses R, as a reference for people who work with machine learning methods, and in industry for exploratory experiments in machine learning.

Geocomputation with R

Author : Robin Lovelace
Publisher : CRC Press
ISBN 13 : 1351396900
Total Pages : 335 pages
Book Rating : 4.3/5 (513 download)

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Book Synopsis Geocomputation with R by : Robin Lovelace

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 335 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/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping. All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS.

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

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.

Machine Learning with R

Author : Brett Lantz
Publisher : Packt Publishing Ltd
ISBN 13 : 1782162151
Total Pages : 587 pages
Book Rating : 4.7/5 (821 download)

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Book Synopsis Machine Learning with R by : Brett Lantz

Download or read book Machine Learning with R written by Brett Lantz and published by Packt Publishing Ltd. This book was released on 2013-10-25 with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

Applied Predictive Modeling

Author : Max Kuhn
Publisher : Springer Science & Business Media
ISBN 13 : 1461468493
Total Pages : 595 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Applied Predictive Modeling by : Max Kuhn

Download or read book Applied Predictive Modeling written by Max Kuhn and published by Springer Science & Business Media. This book was released on 2013-05-17 with total page 595 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

Pattern Recognition and Neural Networks

Author : Brian D. Ripley
Publisher : Cambridge University Press
ISBN 13 : 9780521717700
Total Pages : 420 pages
Book Rating : 4.7/5 (177 download)

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Book Synopsis Pattern Recognition and Neural Networks by : Brian D. Ripley

Download or read book Pattern Recognition and Neural Networks written by Brian D. Ripley and published by Cambridge University Press. This book was released on 2007 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.

Explanatory Model Analysis

Author : Przemyslaw Biecek
Publisher : CRC Press
ISBN 13 : 0429651376
Total Pages : 312 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis Explanatory Model Analysis by : Przemyslaw Biecek

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.

Hands-On Machine Learning with R

Author : Brad Boehmke
Publisher : CRC Press
ISBN 13 : 1000730433
Total Pages : 373 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Hands-On Machine Learning with R by : Brad Boehmke

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.

Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems

Author : Ali M. Eltamaly
Publisher : Springer
ISBN 13 : 3030055787
Total Pages : 486 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems by : Ali M. Eltamaly

Download or read book Modern Maximum Power Point Tracking Techniques for Photovoltaic Energy Systems written by Ali M. Eltamaly and published by Springer. This book was released on 2019-08-01 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces and analyses the latest maximum power point tracking (MPPT) techniques, which can effectively reduce the cost of power generated from photovoltaic energy systems. It also presents a detailed description, analysis, and comparison of various MPPT techniques applied to stand-alone systems and those interfaced with electric utilities, examining their performance under normal and abnormal operating conditions. These techniques, which and can be conventional or smart, are a current hot topic, and this book is a valuable reference resource for academic researchers and industry professionals who are interested in exploring and implementing advanced MPPT for photovoltaic systems. It is also useful for graduate students who are looking to expand their knowledge of MPPT techniques.

Applied Econometrics with R

Author : Christian Kleiber
Publisher : Springer Science & Business Media
ISBN 13 : 0387773185
Total Pages : 229 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Applied Econometrics with R by : Christian Kleiber

Download or read book Applied Econometrics with R written by Christian Kleiber and published by Springer Science & Business Media. This book was released on 2008-12-10 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.

Machine Learning Mastery With R

Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 219 pages
Book Rating : 4./5 ( download)

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Book Synopsis Machine Learning Mastery With R by : Jason Brownlee

Download or read book Machine Learning Mastery With R written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2016-01-30 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: R has been the gold standard in applied machine learning for a long time. Surveys show that it is the most popular platform used by professional data scientists. It is also preferred by the best data scientists in the world. In this Ebook, learn how to get started, practice and apply machine learning using the R platform.

Evolutionary Multi-Criterion Optimization

Author : Heike Trautmann
Publisher : Springer
ISBN 13 : 3319541579
Total Pages : 717 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Evolutionary Multi-Criterion Optimization by : Heike Trautmann

Download or read book Evolutionary Multi-Criterion Optimization written by Heike Trautmann and published by Springer. This book was released on 2017-02-17 with total page 717 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 held in Münster, Germany in March 2017. The 33 revised full papers presented together with 13 poster presentations were carefully reviewed and selected from 72 submissions. The EMO 2017 aims to discuss all aspects of EMO development and deployment, including theoretical foundations; constraint handling techniques; preference handling techniques; handling of continuous, combinatorial or mixed-integer problems; local search techniques; hybrid approaches; stopping criteria; parallel EMO models; performance evaluation; test functions and benchmark problems; algorithm selection approaches; many-objective optimization; large scale optimization; real-world applications; EMO algorithm implementations.

Feature Engineering and Selection

Author : Max Kuhn
Publisher : CRC Press
ISBN 13 : 1351609467
Total Pages : 266 pages
Book Rating : 4.3/5 (516 download)

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Book Synopsis Feature Engineering and Selection by : Max Kuhn

Download or read book Feature Engineering and Selection written by Max Kuhn and published by CRC Press. This book was released on 2019-07-25 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Ensemble Machine Learning

Author : Cha Zhang
Publisher : Springer Science & Business Media
ISBN 13 : 1441993258
Total Pages : 332 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Ensemble Machine Learning by : Cha Zhang

Download or read book Ensemble Machine Learning written by Cha Zhang and published by Springer Science & Business Media. This book was released on 2012-02-17 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics. Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.

Methods of Behavior Analysis in Neuroscience

Author : Jerry J. Buccafusco
Publisher : CRC Press
ISBN 13 : 1420041819
Total Pages : 341 pages
Book Rating : 4.4/5 (2 download)

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Book Synopsis Methods of Behavior Analysis in Neuroscience by : Jerry J. Buccafusco

Download or read book Methods of Behavior Analysis in Neuroscience written by Jerry J. Buccafusco and published by CRC Press. This book was released on 2000-08-29 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using the most well-studied behavioral analyses of animal subjects to promote a better understanding of the effects of disease and the effects of new therapeutic treatments on human cognition, Methods of Behavior Analysis in Neuroscience provides a reference manual for molecular and cellular research scientists in both academia and the pharmaceutic

Water Demand Forecasting

Author : V. Gardiner
Publisher : CRC Press
ISBN 13 : 1482275600
Total Pages : 148 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Water Demand Forecasting by : V. Gardiner

Download or read book Water Demand Forecasting written by V. Gardiner and published by CRC Press. This book was released on 1986-06-30 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an outcome of the workshop on water demand forecasting in 1985. It summarises the 'state-of-the-art' in water demand forecasting, and identifies some of its links with environmental issues. The book discusses some of the issues raised in more detail and provides case studies.