SKU/Artículo: AMZ-B0G7HYRYXT
Applied Machine Learning in R: A Comprehensive Guide: Modeling, Evaluation, and Predictive Analytics for Data-Driven Decision Making
Format:
Paperback
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En stock
En stock
Peso con empaque:
0.68 kg
0.68 kg
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Sí
Sí
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Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- Reactive PublishingMachine learning is no longer an abstract research discipline. It is a practical toolset used daily in finance, economics, healthcare, technology, and scientific research. This book is a rigorous, application-focused guide to building, evaluating, and deploying machine learning models using R, with an emphasis on clarity, correctness, and real-world decision making.The book begins by grounding machine learning in its statistical foundations, ensuring readers understand not just how models work, but why they work. Core concepts such as bias–variance tradeoffs, overfitting, feature engineering, and model evaluation are explained in practical terms before being applied in code. Readers are guided through regression, classification, and unsupervised learning techniques using modern R workflows that reflect professional practice.Rather than relying on isolated examples, this book emphasizes end-to-end modeling pipelines. Readers learn how to prepare data, select appropriate algorithms, tune models, validate results, and interpret outputs in ways that support reliable conclusions. Frameworks such as caret and tidymodels are introduced to demonstrate scalable, reproducible machine learning workflows that can be adapted across domains.Special attention is given to model evaluation and interpretation. Topics such as cross-validation, performance metrics, feature importance, and diagnostic analysis are treated as first-class concerns, not afterthoughts. The goal is not to maximize benchmark scores, but to build models that can be trusted in real analytical and business contexts.Designed for analysts, data scientists, researchers, and technically inclined professionals, this book assumes familiarity with R fundamentals and basic data analysis. It is not a collection of shortcuts or black-box recipes. Instead, it provides a structured path from foundational concepts to applied machine learning competence, equipping readers to build models responsibly, understand their limitations, and deploy them with confidence.This book serves as a bridge between statistical thinking and modern machine learning practice in R, preparing readers for advanced specialization while reinforcing the disciplined mindset required for high-quality analytical work.
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AR$91.841
31% OFF
AR$63.336
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