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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchFeature Engineering and Selection: A Practical Approach for Predictive Models by Max Kuhn and Kjell Johnson is a practical, R-oriented book about shaping predictors and choosing which ones to use in predictive models. Its listed coverage moves from modeling workflow and data preparation through feature-selection techniques. That breadth makes it a relevant option for readers who want to see feature work in context; the publisher’s description alone, however, cannot establish how clearly the book teaches each topic or whether it is the right fit for every reader.
What the book is about
Feature engineering changes how a model represents input variables; feature selection chooses a subset of those variables for modeling. The book treats both as parts of predictive modeling rather than as isolated preprocessing steps. Its contents begin with introductory material and a predictive-modeling workflow, then progress through applied examples and methods for preparing and selecting features.
The publisher says the book uses example datasets and R programs to reproduce results. This makes the stated approach applied as well as methodological: readers are meant to encounter techniques alongside data and code, rather than only as abstract descriptions. The available descriptions do not establish how much R knowledge the authors assume or how much conceptual explanation accompanies each program.
What topics does it cover?
The publisher’s contents outline a broad sequence, from understanding the modeling task to transforming predictors and selecting them. Topics include:
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- An ischemic-stroke prediction example, model performance, and data splitting.
- Resampling and overfitting, followed by exploratory visualization.
- Categorical encoding, numeric feature engineering, and interaction effects.
- Missing data and profile data.
- Feature-selection approaches, including simple filters, recursive feature elimination, stepwise selection, simulated annealing, and genetic algorithms.
The combination is useful to consider when deciding what you want from a book: it spans data preparation and workflow as well as selection algorithms. Its contents list does not demonstrate that any one method is universally best; appropriate feature work depends on the modeling problem and the evaluation process.
Who may find it useful?
The book appears suited to readers building predictive models who want a practical account of representing predictors and selecting subsets, with examples in R. Its coverage may be a better match for someone seeking a connected treatment of workflow, preprocessing, and selection than for someone looking only for a deep reference on one specific algorithm.
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The available publisher and catalog descriptions do not specify formal prerequisites. They are not enough to label the book definitively beginner-friendly or advanced. Readers should consider their own comfort with predictive modeling and R before choosing it, and consult a sample or the full text if they need to judge the level of explanation.
Is it worth reading?
It is a plausible choice if your goal is to study feature engineering and feature selection together, in the context of predictive modeling, and you value worked examples with R code. Its listed range—from data splitting and resampling to feature transformations and several selection strategies—gives prospective readers a concrete basis for comparing it with a narrower method-specific reference.
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A firm quality verdict would require reading the edition in question. The publisher’s description and contents establish the book’s intended scope and format, not the accuracy of every explanation, the ease of reproducing every result, or the learning outcomes readers can expect. Nor do the available sources support a claim that its methods outperform alternatives in general.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Edition and bibliographic details
Edition records give different publication and page-count details, so identify the edition by format and ISBN rather than treating those figures as universal. The 2019 Google Books listing records 310 pages; a separate 2021 CRC Press/Taylor & Francis reprint listing records 314 pages. Google Books’ 2019 record identifies the print ISBN as 9781351609470 and the ebook ISBN as 9781351609463. The publisher lists the print edition under ISBN 9781032090856; consult its Routledge / Taylor & Francis book page for that edition’s details.
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A review in The American Statistician provides additional review context for the book and its subject. It should not be treated as evidence for claims beyond what its accessible text supports.
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