Foundations of Predictive Analytics (Hardcover)

James Wu, Stephen Coggeshall

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商品描述

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, naïve 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 book’s website at www.DataMinerXL.com offers the DataMinerXL software for building predictive models. The site also includes more examples and information on modeling.

商品描述(中文翻譯)

憑藉作者們在應用建模和數據挖掘方面的二十年經驗,《預測分析基礎》提供了分析數據和建立模型所需的基本背景,適用於許多實際應用,如消費者行為建模、風險和市場分析等領域。它還討論了許多常見教材中經常缺少的實用主題。

本書首先介紹了建模方法的統計和線性代數/矩陣基礎,從分佈到累積量和copula函數,再到Cornish-Fisher擴展和其他有用但難以找到的統計技術。然後描述了常見和不尋常的線性方法,以及流行的非線性建模方法,包括加法模型、樹、支持向量機、模糊系統、聚類、朴素貝葉斯和神經網絡。作者們還介紹了時間序列和預測中使用的方法,如ARIMA、GARCH和生存分析。他們還介紹了一系列優化技術,並探討了幾個特殊主題,如Dempster-Shafer理論。

作為預測分析中最重要的基礎材料的深入收集,這本自成一體的書提供了理解探索性數據分析和建模各種技術所需的信息。它解釋了每種技術背後的算法細節(包括基本假設和數學公式),並展示了如何準備和編碼數據、選擇變量、使用模型優良度量、規範概率、進行拒絕推斷。

網站www.DataMinerXL.com提供了用於建立預測模型的DataMinerXL軟件。該網站還提供更多示例和建模信息。