STATISTICAL AND MACHINE-LEARNING DATA MINING BRUCE RATNER PDF

ata M in in g. Statistical and. Machine-Learning. Data Mining. Bruce Ratner. Techniques for Better Predictive Modeling and Analysis of Big Data. Second Edition. This book by Bruce Ratner distinguishes between statistical data mining and machine-learning data mining, and explains GenIQ Model, a machine-learning. Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data Bruce Ratner, CRC Press/Taylor & Francis.

Author: Goltikus Gura
Country: Guatemala
Language: English (Spanish)
Genre: Health and Food
Published (Last): 13 July 2013
Pages: 349
PDF File Size: 2.37 Mb
ePub File Size: 3.92 Mb
ISBN: 277-2-30328-782-1
Downloads: 30846
Price: Free* [*Free Regsitration Required]
Uploader: Kele

Learn More about VitalSource Bookshelf. You can find the first often; the second occasionally; but the third, esp.

An Easy Way to Understand the Segments Data Mining to Uncover Innards of a Model Includes SAS subroutines which can be easily converted to other languages. Descriptive, Predictive, and Look-Alike Profiling In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques.

Ignorable Problem, Notable Solution Assessment of Marketing Models Interpretation of Coefficient-Free Models Validating the Logistic Regression Model: Visualization of Marketing Models: This book is an excellent contribution to the literature of statistics, data mining, and machine learning.

TOP Related Articles  EJERCICIOS DE GRAVIMETRIA RESUELTOS PDF

The country you have selected will result in the following: The Importance of Straight Data: It could be through conference attendance, group discussion or directed reading to name just a few examples.

The title will be removed from your cart because it is not available in this region. Net T-C Lift Model: CPD consists of any educational activity which helps to maintain and develop knowledge, problem-solving, and technical skills with the aim to provide better health care through higher standards.

Book: Statistical and Machine-Learning Data Mining, by Bruce Ratner

Handbook of Big Machine-learjing. A New Approach for Validating Models What are VitalSource eBooks? A Twelve-Step Program for Dataholics Finding the Best Variables for Marketing Models Perspective and Performance The Workhorse of Profit Modeling Old Problem, New Solution We provide complimentary e-inspection copies of primary textbooks to instructors considering our books for course adoption.

The book is a valuable resource for experienced and newbie data scientists.

Science Dealing with Data: Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical machine-learnihg Machine-Learning Data Mining: I cannot thank you enough, Bruce! Statistics and Data Science 3.

Add to Wish List. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.

TOP Related Articles  ECCLESIASTICAL ENDORSEMENT BYU PDF

For Instructors Request Inspection Copy. He holds a patent for a unique application in solving the two-group classification problem with genetic programming.

It offers many insightful perspectives to use for future ALM features and improvements. Art, Science, Mlning, and Poetry Request an e-inspection copy. Offline Computer — Download Bookshelf software to your desktop so you can view your eBooks with or without Internet access.

Already read this title? The Statistical Regression Model: An Easy Way to Understand the Model Identifying Your Best Customers: Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing NLP.