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59940114
Applied Predictive Modeling, (Paperback)
98% of respondents would recommend this to a friend
Nº de artículo:
59940114
ARS 130581
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Applied Predictive Modeling provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems.
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What Stands Out
Comprehensive Approach
Covers a wide range of predictive modeling techniques, offering readers an all-encompassing guide from basic concepts to advanced applications, ideal for both novices and experienced practitioners.
Practical Examples
Includes real-world case studies and hands-on exercises that enhance learning, allowing readers to apply concepts effectively and understand practical implications in various fields.
Expert Insights
Authored by leading experts in predictive modeling, providing credibility and invaluable insights that equip readers with the knowledge to tackle complex predictive challenges with confidence.
Detalles de producto
- Applied Predictive Modeling 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. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner's reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book's R package.This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Textbooks |
| Publication date | March, 2019 |
| Pages | 600 |
| Subgenre | Probability & Statistics |
| Series title | No Series |
| Edition | softcover reprint of the original 1st 2013 Edition |
| Publisher | Springer New York |
| Original languages | English |
| Language | English |
| Edu focus | Mathematics |
| Educational level | General |
| Is collectible | N |
| Binding type | Perfect Binding |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 6.14 x 1.24 x 9.21 in (15.6 x 3.1 x 23.4 cm) |
| Assembled product weight | 1.87 lb (850 grams) |
| Bisac subject heading | Mathematics |
Who Should Buy?
-
Aspiring Data Scientists
Ideal for those looking to develop skills in predictive modeling using real-world data applications and techniques.
-
Graduate Students
Great resource for students pursuing advanced studies in statistics, machine learning, and data analytics.
-
Industry Professionals
Beneficial for those in data analysis roles, seeking to enhance their practical understanding of predictive modeling.
-
Beginner Learners
May be too advanced for those without a foundational understanding of statistics and data analysis concepts.
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Max Kuhn All Books Editorial Review
Applied Predictive Modeling is a comprehensive paperback text that delves into the intricacies of statistical methodologies and practical applications in the field of predictive modeling. This non-fiction book, published by Springer New York in March 2019, spans 600 pages and is particularly valuable for those with a background in mathematics and statistics. Designed for general educational levels, it serves as a vital resource for understanding various aspects of probability and statistics. Readers have praised its clarity in presenting complex topics, making it a go-to guide for students and professionals alike seeking to enhance their predictive modeling skills.
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ventajas
- In-depth coverage of predictive modeling techniques
- Suitable for both students and professionals
- Clear and concise explanations of complex concepts
- Well-structured with extensive examples
- Perfect binding for durability
Contras
- Some readers may find it dense and detailed
Product Price History
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ARS 130581
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características y beneficios
- Comprehensive guide to predictive modeling.
- Covers data preprocessing, splitting, and model tuning.
- Emphasizes real-world applications and problem-solving.
- Includes extensive R code for hands-on experience.
- Suitable for both beginners and advanced learners.
- Offers problem sets to reinforce learning.
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