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Author: Max Kuhn
ISBN : B00CU4Q4QS
New from $25.08
Format: PDF, EPUB
Posts about Download The Book Free Applied Predictive Modeling from with Mediafire Link Download LinkThis 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.Direct download links available for Free Applied Predictive Modeling [Kindle Edition]
- File Size: 7141 KB
- Print Length: 620 pages
- Publisher: Springer; 2013 edition (May 17, 2013)
- Sold by: Amazon Digital Services, Inc.
- Language: English
- ASIN: B00CU4Q4QS
- Text-to-Speech: Enabled
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- Lending: Not Enabled
- Amazon Best Sellers Rank: #320,684 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Free Applied Predictive Modeling
tl;dr: A brilliant book covering Predictive modelling in R. With a strong practical bent it walks the reader through the application of modern classification and regression techniques to a broad number of varied and interesting data sets. It uses existing packages where possible so you can jump straight in (great for Kagglers) but there is a lot here to master. It is especially strong on preprocessing (both unsupervised and supervised), model tuning and model assessment. Should not be your first book on R or data analytics but the best balance of Practical application without foregoing theory that I have seen. It is wonderful to see how professional data analysts approach predictive modelling tasks. The data sets are not toy models to highlight approaches but interesting and complex problems from a wide variety of disciplines.(Note that this book does not cover Time Series, Generalised Additive Models and Ensemble's of different models).
Review:
Data science has become very popular due to the increase in computing power (including things like AWS), the amount of data that is accessible on the internet and a number of open-source tools (R and Python for example) that allow even relative beginners to complete quite sophisticated models. Coursera allows for one to complete courses on Machine Learning for free and sites like Kaggle have even turned it into something of a sport where people compete to create predictive models for money or even job interviews. Part of the excitement is that Predictive models can be applied to almost any field you can think of.
There are many fine math-oriented predictive modeling books, such as Hastie (The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)). Kuhn et al consider them "sister texts" and begin immediately to differentiate-- their approach is hands on and practical, for the express purpose of demonstrating HOW to sort, structure and predict via Python or R, for the purpose of accuracy and understanding of the DATA and trends, NOT learning the underlying math.
For a couple of pharmaceutical guys, (who BTW use R extensively, I've been an analyst in that industry), you'd think the examples would be new chemical or biological entities. Not so! The cases are fun and exciting, ranging from the nontrivial compression strength of concrete (want that bridge to hold when you cross?) to fuel economy, credit scoring, success in grant applications (boy their colleagues will love that one!), and cognitive impairment. I evaluate technology for patents at payroy dot com, and we have a log likelihood model using Bayesian and Monte Carlo that their grant section helped translate seamlessly to R! We're NOT talking pie in the sky pseudo code here, but real life, real results recipes.
The authors talk about the "scholarly veil" -- meaning we general workers and researchers don't always "deserve" to see the underlying process, software and data (and, other than open source, often can't afford it). Wow, do they pop that myth!