Applied Predictive Modeling by

machinelearningmastery.com · Unknown · 2025-10-23

good 4.00

The book is praised for its practical approach to predictive modeling, making it accessible to both beginners and experienced practitioners. It is noted for its clear explanations and useful examples, though some may find the coverage of advanced topics lacking.

They found the book to be an excellent resource for learning predictive modeling, particularly for those new to the field. The authors do a great job of breaking down complex concepts into understandable terms, and the practical examples are very helpful. However, they felt that the book could have delved deeper into more advanced topics to cater to a wider audience. Overall, it's a solid introduction to predictive modeling that provides a good foundation for further study.

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The book is a great introduction to predictive modeling

It's very practical and easy to follow

The examples are particularly useful for understanding the concepts.

academic.oup.com · Unknown · 2018-01-01

great 4.50

The book is praised for its practical approach to predictive modeling, with a focus on real-world applications and clear presentation of complex topics. It is recommended for both beginners and experienced practitioners in the field.

This book is highly regarded for its hands-on approach to predictive modeling. It covers a wide range of topics, from general strategies to specific methods for regression and classification, all presented in a practical and accessible manner. The book is not overly academic, focusing instead on real-world applications and providing plenty of examples with available datasets and code. This makes it easier for readers to follow along and apply the concepts to their own work. The separation of discussion and code also enhances the learning experience, allowing readers to focus on the material without getting bogged down in technical details. Overall, it is a valuable resource for anyone interested in predictive modeling, offering both the big picture and detailed insights.

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This is a gem of a book.

It is not an academic or mathematical treatise; the emphasis is on practice, discussing the issues that commonly arise and how they can be approached.

This is a great book, providing both the trees and the forest so to speak.

r-bloggers.com · Unknown · 2014-06-26

excellent 4.50

Applied Predictive Modeling by Kuhn and Johnson is highly praised for its rigorous analysis, careful thinking, and good prose. The book is recommended for those with a background in statistics who are serious about building predictive models. It emphasizes accurate predictions, thorough case studies, and the use of the R language, making it a valuable resource for mastering predictive modeling.

Applied Predictive Modeling by Kuhn and Johnson is a standout book in the field of predictive modeling. The authors begin by defining predictive modeling as the process of developing models to understand and quantify prediction accuracy on future data. They emphasize the importance of accurate predictions over easily interpreted models, while also stressing the need for intuition and deep knowledge of the problem context. The book is praised for its rigorous analysis, careful thinking, and clear prose, making it a valuable resource for those serious about building predictive models. The authors' approach is intense but not oppressive, and they write with great clarity, even though the material is challenging. The book is organized into sections that focus on specific models while reinforcing fundamental principles. It includes thorough case studies and extensive use of the R language, making it well-suited for self-study. The book is also seen as an introduction to the caret package, offering great depth and functionality for R's machine learning capabilities.

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The authors begin their book by stating that “the practice of predictive modeling defines the process of developing a model in a way that we can understand and quantify the model’s prediction accuracy on future, yet-to-be-seen data”.

Kuhn and Johnson are intense but not oppressive. They come across like coaches who really, really want you to be able to do this stuff.

The authors point out that the estimates of bad credit in the right panel are skewed showing that most estimates predict very low probabilities for bad credit when the credit is, in fact, good — just what you want to happen.

r-bloggers.com · Unknown · 2013-11-24

excellent 4.50

Applied Predictive Modeling is a highly recommended book for machine learning practitioners and R users. It teaches practical machine learning theory with code examples in R, focusing on building models from real-world data to make predictions. The book is well-structured and written by skilled authors, making it a fantastic reference for anyone looking to improve their predictive modeling skills.

Applied Predictive Modeling is an excellent resource for anyone interested in machine learning and predictive modeling. The book is written by Max Kuhn and Kjell Johnson, both highly skilled professionals in the field. It teaches practical machine learning theory with code examples in R, making it accessible and useful for practitioners. The book is well-structured, covering general strategies, regression models, and classification models. It focuses on building models from real-world data to make predictions, which is a crucial objective in the field. The authors' expertise and the book's practical approach make it a fantastic reference for anyone looking to improve their predictive modeling skills.

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It is an excellent book and highly recommended to machine learning practitioners and users of R for machine learning.

The reason it was and still is in such great demand is because it is a fantastic reference written by very skilled authors.

The book is broken down into 4 parts: General Strategies, Regression Models, Classification Models, and Practical Modeling.

Applied Predictive Modeling

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