Applied Predictive Modeling Kuhn Pdf [UPD] Download
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How to Download Applied Predictive Modeling by Max Kuhn and Kjell Johnson in PDF Format
Applied Predictive Modeling is a textbook that 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.
If you are interested in learning more about predictive models and how to apply them to real data, you might want to download Applied Predictive Modeling by Max Kuhn and Kjell Johnson in PDF format. Here are some ways you can do that:
You can purchase the ebook version from SpringerLink[^2^], which is the official publisher of the book. You will need to create an account and pay a fee to access the full text. You can also preview some chapters for free.
You can borrow the ebook version from Internet Archive[^3^], which is a non-profit library of millions of free books, movies, software, music, websites, and more. You will need to create an account and join a waiting list to access the full text. You can also preview some pages for free.
You can download the supplementary material from the book's website[^4^], which includes R code, data sets, exercises, and solutions. You will not need to create an account or pay a fee to access these resources.
Whichever option you choose, we hope you enjoy reading Applied Predictive Modeling by Max Kuhn and Kjell Johnson and learn a lot from it!
Applied Predictive Modeling by Max Kuhn and Kjell Johnson is divided into four parts: General Strategies, Regression Models, Classification Models, and Other Considerations. Each part contains several chapters that cover different aspects of the predictive modeling process and various techniques that can be used for different types of data and problems. Here is a brief overview of each part and chapter:
General Strategies: This part introduces the basic concepts and steps of predictive modeling, such as data preprocessing, data splitting, model tuning, and performance evaluation. It also provides a short tour of the predictive modeling process using a simple example.
Chapter 1: Introduction
Chapter 2: A Short Tour of the Predictive Modeling Process
Chapter 3: Data Pre-processing
Chapter 4: Over-Fitting and Model Tuning
Regression Models: This part focuses on models that predict a continuous outcome variable, such as linear regression, nonlinear regression, regression trees, and rule-based models. It also includes two case studies that illustrate how to apply regression models to real data problems.
Chapter 5: Measuring Performance in Regression Models
Chapter 6: Linear Regression and Its Cousins
Chapter 7: Nonlinear Regression Models
Chapter 8: Regression Trees and Rule-Based Models
Chapter 9: A Summary of Solubility Models
Chapter 10: Case Study: Compressive Strength of Concrete Mixtures
Classification Models: This part focuses on models that predict a categorical outcome variable, such as discriminant analysis, logistic regression, nonlinear classification models, classification trees, and rule-based models. It also includes a case study and a chapter on how to deal with severe class imbalance problems.
Chapter 11: Measuring Performance in Classification Models
Chapter 12: Discriminant Analysis and Other Linear Classification Models
Chapter 13: Nonlinear Classification Models
Chapter 14: Classification Trees and Rule-Based Models
Chapter 15: A Summary of Grant Application Models
Chapter 16: Remedies for Severe Class Imbalance
Other Considerations: This part covers some additional topics that are relevant for predictive modeling, such as feature selection, interaction detection, correlation and collinearity, missing data imputation, and ensemble models.
Chapter 17: Feature Selection
Chapter 18: Detecting Interaction Effects
Chapter 19: Correlation and Collinearity
Chapter 20: Missing Data Imputation
Appendix A: An Introduction to Ensemble Models aa16f39245