Feature Engineering and Selection (Chapman & Hall/CRC Data Science Series)
In Feature Engineering and Selection, Max Kuhn and Kjell Johnson deliver a comprehensive guide to crafting and refining features for data science and machine learning. Part of the Chapman & Hall/CRC Data Science Series, it covers techniques to transform data effectively, select optimal variables, and boost model performance in real-world applications.
About This Book
Feature Engineering and Selection is part of the Chapman & Hall/CRC Data Science Series, authored by Max Kuhn and Kjell Johnson. It focuses on the critical processes involved in preparing data for modeling in data science applications.
The book addresses methods for creating and choosing features that enhance the accuracy and efficiency of machine learning algorithms. Readers gain insights into handling various data types and selecting the most relevant variables.
Designed for practitioners, it provides actionable strategies to streamline workflows and avoid common pitfalls in feature preparation. The content emphasizes practical implementation within data science pipelines.
Overall, this resource supports data professionals in building robust models by optimizing the feature engineering stage.
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