[ Pdf Applied Predictive Modeling ò art-books-monographs PDF ] by Max Kuhn × globalintertrade.co.uk

[ Pdf Applied Predictive Modeling ò art-books-monographs PDF ] by Max Kuhn × 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 The authors explain that their coverage of predictive modeling includes machine learning, pattern recognition, and data mining, and expands to a broader guide to the process of developing models and quantifying their predictive accuracies.
A major theme throughout the book is detection of overfitting Techniques to manage overfitting are discussed in detail These include data preprocessing, normalization, standardization, transformation of distributions, feature selection, train test split, cross validation, goodness of fit, and error metrics.
Linear and non linear models are described, with detailed examples of use with actual data.
The illustrations are superb Fully disclosed code in R is included.
This book is a very readable handbook that I highly recommend to everyone developing predictive models.
I wish I d had this book 10 years ago, and the discipline to have sat down and read it thoroughly It is well written, has beautiful plots that are worthy of a book on visualization all by themselves, has great coverage of topics, and is easy to understand.
There is a natural comparison to be made toThe Elements of Statistical Learning Data Mining, Inference, and Prediction, Second Edition Springer Series in Statistics I found this book much, much better Where ESLII was fractured and seemed to jump from point to point with no explanation, APM proceeded in a well thought out manner ESLII used some non standard notation and assumptions, where APM used notation familiar to anyone with a background in statistics and linear algebra To be fair, it may be that I ll return to ESL after having read APM and be able to bridge the leaps the authors made with material I ve learned from this book.
The pros Gives a solid introduction to the problem prediction is trying to solve Provides a framework for evaluating prediction results, using a consistent data set across all problems Has citations and references for further reading Does a good job of contrasting machine learning black box models and classical statistics interpertability see Breiman s Statistical Modeling Two Cultures paper for some great insights into this phenomenon The cons A bit light on theory, especially proofs and details behind the models I feel this is a bit of a pro, though, since the citations for the work are provided, and the theorems and proofs are there if you are interested in them.
My name is Matt I m an educator that focuses on data science in business applications My background is business and mechanical engineering, not computer science I don t have a PhD I m an ordinary person that fell in love with Data Science I ve sense started an education business aimed at bringing applied data science courses to help business minded people solve real world problems.
I purchased Applied Predictive Modeling after visiting a high performance hedge fund that employs a number of brilliant minds This book appeared in most of the work spaces so I decided to pick up a copy and read it for myself.
I read the first half of APM on vacation and honestly I couldn t put it down The book goes into detail on a wide range of models, many of which I d never heard of before Beyond this, APM provides the R code showing exactly how to implement the models For me, this application focus is valuable.
The book weaves in many case studies from pharmaceuticals, to business, to even using machine learning to find the optimal concrete formula.
I will say that this book is not for complete beginners, but as soon as you get through the basics this is a great book from two of the best minds in modeling For beginners I recommend R For Data Science.
Hope this helps Matt While this was largely a review for me, there are always gems to be found in comprehensive texts like this I would have loved to have this book 6 7 years ago Even though I don t agree with the entirety of the espoused approach see e.
g Practical Data Science with R for an alternative approach to the cross validation test train holdout set , it is a valid one and I highly recommend this to anyone implementing supervised learning models In particular, the author s caret package which is a perfect companion to this book provides a great basis for data model pipelining that I would dearly love to see other ML frameworks adopt scikit learn is close, but not quite there , and will provide a practical baseline for those building custom model pipelines and frameworks or evaluating what is available off the shelf.
After completing Introduction to Statistical Learning with applications in R, this takes the study of predictive modeling to a new level using the caret package in R It is so much fun to read and experiment with that I carry it in my backpack, and I read it everywhere including before going to sleep at night.