Wu, James Coggeshall, Stephen Foundations of predictive analytics 9780367381684

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Автор: Wu, James Coggeshall, Stephen
Название: Foundations of predictive analytics
ISBN: 9780367381684
Издательство: Taylor&Francis
Классификация:

Вероятность и статистика

Техника автоматического регулирования

Извлечение знаний из данных

Теория вычислительных машин и систем, кибернетика

ISBN-10: 0367381680
Обложка/Формат: Paperback
Страницы: 338
Вес: 0.63 кг.
Дата издания: 05.09.2019
Язык: English
Размер: 155 x 236 x 26
Читательская аудитория: Tertiary education (us: college)
Рейтинг:
Поставляется из: Европейский союз
Описание:

Drawing on the authors two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It also discusses a variety of practical topics that are frequently missing from similar texts.

The book begins with the statistical and linear algebra/matrix foundation of modeling methods, from distributions to cumulant and copula functions to Cornish-Fisher expansion and other useful but hard-to-find statistical techniques. It then describes common and unusual linear methods as well as popular nonlinear modeling approaches, including additive models, trees, support vector machine, fuzzy systems, clustering, naпve Bayes, and neural nets. The authors go on to cover methodologies used in time series and forecasting, such as ARIMA, GARCH, and survival analysis. They also present a range of optimization techniques and explore several special topics, such as Dempster-Shafer theory.

An in-depth collection of the most important fundamental material on predictive analytics, this self-contained book provides the necessary information for understanding various techniques for exploratory data analysis and modeling. It explains the algorithmic details behind each technique (including underlying assumptions and mathematical formulations) and shows how to prepare and encode data, select variables, use model goodness measures, normalize odds, and perform reject inference.

Web Resource
The books website at www.DataMinerXL.com offers the DataMinerXL software for building predictive models. The site also includes more examples and information on modeling.

Foundations of predictive analytics, Wu, James Coggeshall, Stephen