Ratner Statistical and Machine-Learning Data Mining

Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition
3. Auflage 2017
ISBN: 978-1-4987-9761-0
Verlag: CRC Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 690 Seiten

The third edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. is a compilation of new and creative data mining techniques, which address the scaling-up of the framework of classical and modern statistical methodology, for predictive modeling and analysis of big data. SM-DM provides proper solutions to common problems facing the newly minted data scientist in the data mining discipline. Its presentation focuses on the needs of the data scientists (commonly known as statisticians, data miners and data analysts), delivering practical yet powerful, simple yet insightful quantitative techniques, most of which use the "old" statistical methodologies improved upon by the new machine learning influence.

Autoren/Hrsg.

Ratner, Bruce

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

Introduction. Two Basic Data Mining Methods for Variable Assessment. CHAID-based Data Mining for Paired-Variable Assessment. The Importance of Straight Data: Simplicity and Desirability for Good Model-Building Practice. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment. The Correlation Coefficient: Its Values Range Between Plus/Minus 1, or Do They? Logistic Regression: The Workhorse of Response Modeling. Ordinary Regression: The Workhorse of Profit Modeling. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution. CHAID for Interpreting a Logistic Regression Model. The Importance of the Regression Coefficient.The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables. CHAID for Specifying a Model with Interaction Variables. Market Segmentation Classification Modeling with Logistic Regression. CHAID as a Method for Filling in Missing Values. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling. Assessment of Marketing Models. Bootstrapping in Marketing: A New Approach for Validating Models. Validating the Logistic Regression Model: Try Bootstrapping. Visualization of Marketing Models: Data Mining to Uncover Innards of a Model. The Predictive Contribution Coefficient: A Measure of Predictive Importance. Regression Modeling Involves Art, Science, and Poetry, Too. Genetic and Statistic Regression Models: A Comparison. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model. A Data Mining Method for Moderating Outliers Instead of Discarding Them. Overfitting: Old Problem, New Solution. The Importance of Straight Data: Revisited. The GenIQ Model: Its Definition and an Application. Finding the Best Variables for Marketing Models. Interpretation of Coefficient-Free Models.

Über Autor(innen)

Bruce Ratner, The Significant StatisticianTM, is President and Founder of DM STAT-1 Consulting, the ensample for Statistical Modeling, Analysis and Data Mining, and Machine-learning Data Mining in the DM Space. DM STAT-1 specializes in all standard statistical techniques, and methods using machine-learning/statistics algorithms, such as its patented GenIQ Model, to achieve its clients' goals – across industries including Direct and Database Marketing, Banking, Insurance, Finance, Retail, Telecommunications, Healthcare, Pharmaceutical, Publication & Circulation, Mass & Direct Advertising, Catalog Marketing, e-Commerce, Web-mining, B2B, Human Capital Management, Risk Management, and Nonprofit Fundraising. Bruce holds a doctorate in mathematics and statistics, with a concentration in multivariate statistics and response model simulation. His research interests include developing hybrid-modeling techniques, which combine traditional statistics and machine learning methods. He holds a patent for a unique application in solving the two-group classification problem with genetic programming.