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 ProcessThis 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 PackageThis 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 If you already know a lot about Statistics, then this book will open your mind to the rapidly growing field of Predictive Analytics It all starts with Linear Regression for which I recommend Linear Regression Analysis by George Seber.
Moving on you will soon encounter Neural Networks which turns out to be a special case of Non Linear Regression Classification is covered, but machine learning is not precisely mapped out Box Jenkins modeling is also not covered, and for that I recommend the 4th edition of Time Series Analysis by Box, Jenkins, and Reinsel where the latter author also contributed to the 3rd edition.
Applied Predictive Models is engaging, instructive and motivating Very valuable read for anyone studying machine learning I found this book complemented the Machine Learning open online course by Coursera Johns Hopkins The authors demonstrate their extensive experience through practical advice throughout the book The book has a good mix of application with introduction to theory by illustration, but is not too heavy on maths for domain specialists to enjoy Every chapter ends with a section on application of the methods using R, so the book can be read with a cup of coffee and laptop beside you to try out what you are reading.
Para quem quer se aprofundar em ferramentas de modelagem preditiva eu recomendo esse livro Embora tenha o vis do autor de ci ncias biolgicas o livro apresenta vrias aplica es de tcnicas estatsticas de modelagem preditiva Figuras coloridas de bastante qualidade e muito comando em R Gostei muito dessa aquisi o
This books fills a useful gap between the basic cookbooks and the advanced theoretical textbooks The authors do a good job in taking you through realistic case studies to show the issues involved with data analysis It definitely helped me get a good feel for both how to apply a range of models and how to use a range of R packages The book is not perfect and some of the data pre processing work was too tedious for my liking It helped to have the extended code handy that is included in the package as it is not the same as that published in the book The latter has some gaps once a while.
The book pairs well with the Elements of Statistical Learning which is what the publisher probably attempted as it addresses similar methods.
I hope that someone writes a similar book but focused on Bayesian machine learning methods.
Applied Predictive Modeling by Max Kuhn and Kjell Johnson is a complete examination of essential machine learning models with a clear focus on making numeric or factorial predictions On nearly 600 pages, the Authors discuss all topics from data engineering, modeling, and performance evaluation.
The core of Applied Predictive Modeling consists of four distinct chapters 1 General Strategies on how to manipulate and re sample data.
2 Regression Models for making numeric predictions.
3 Classification Models for making factor predictions.
4 Other Considerations concerning model quality.
Overall, Applied Predictive Modeling is a very informative course on machine learning It assumes some prior knowledge and might be difficult to access for someone without any knowledge, despite leaving out unnecessary equations Introduction to Statistical Learning by Robert Tibshirani and Trevor Hastie would be a good read before starting this book Some of the book s examples are taken from the field of medicine and pharmaceuticals which make them hard to understand for people outside of the realm of the health sciences.
However, the book does a very good job at making machine learning in R much systematic It clearly shows the advantages of using the caret package written by the book s author and how to evaluate and tune your model s performance.
If you are not entirely new to data science, this book will yield a high return for you It makes your process of training a model straightforward and thorough.