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The Prediction Lover’s Handbook

January 7, 2009 | Thomas H. Davenport, Jeanne G. Harris | Data, AI, & Machine Learning

When picking assessment tools to inform better decisions about future paths, executives are faced with a wide variety of options — some of which are well established, while others are in early stages of development. The authors provide an insider’s guide to prediction and recommendation techniques and technologies. They cover prediction tools including attributized Bayesian analysis, biological responses analysis, cluster analysis, collaborative filtering, content-based filtering/decision trees, neural network analysis, prediction (or opinion) markets, regression analysis, social network-based recommendations and textual analytics. With each potential tool, they briefly describe the technique, who uses it and for what purpose, its strengths and weaknesses, and its future prospects as a prediction tool. Finally, the authors offer up an indication of the best time in the decision process to begin using the tool.

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