標題: Titlebook: Applying Predictive Analytics; Finding Value in Dat Richard V. McCarthy,Mary M. McCarthy,Wendy Ceccucc Textbook 2022Latest edition The Edit [打印本頁] 作者: 習慣 時間: 2025-3-21 18:52
書目名稱Applying Predictive Analytics影響因子(影響力)
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作者: exophthalmos 時間: 2025-3-22 00:09 作者: ALTER 時間: 2025-3-22 02:05 作者: 使長胖 時間: 2025-3-22 08:07
Finding Associations in Data Through Cluster Analysis,in the same cluster. In this chapter several clustering techniques are explained. A simple example is used to explain the different clustering methods. Finally, clustering is applied to a subset of the automobile insurance data set.作者: 令人不快 時間: 2025-3-22 10:27 作者: 情感 時間: 2025-3-22 15:00 作者: Coronation 時間: 2025-3-22 17:35
https://doi.org/10.1007/978-3-030-71644-8dictive models resulting in key output fit statistics that are used to solve business problems. This chapter defines essential analytics terminology, walks through the nine-step process for building predictive analytics models, introduces the “Big 3” techniques, and discusses careers within business analytics.作者: cochlea 時間: 2025-3-23 00:22 作者: 希望 時間: 2025-3-23 04:16 作者: Acumen 時間: 2025-3-23 09:27 作者: 獨行者 時間: 2025-3-23 11:03
Studying Discourse Implies Studying Equityatthews 2018). That results in over 4.3 petabytes of data per year. It is one example of a very large source of unstructured data that contains an enormous amount of information about subjects such as product reviews, perceptions of organizations, issues of importance to consumers, and new product or service introductions.作者: –DOX 時間: 2025-3-23 15:17
sing predictive analytics.Uses examples in SAS Enterprise Mi.The new edition of this textbook presents a practical, updated approach to predictive analytics for classroom learning. The authors focus on using analytics to solve business problems and compares several different modeling techniques, all作者: Mobile 時間: 2025-3-23 20:17 作者: 膝蓋 時間: 2025-3-23 22:17 作者: 木訥 時間: 2025-3-24 04:45 作者: 柔軟 時間: 2025-3-24 07:53
https://doi.org/10.1007/978-3-319-72066-1pwise) and examination of model coefficients are also discussed. The chapter also provides instruction on implementing regression analysis using SAS Enterprise Miner? with a focus on evaluation of the output results.作者: 狂怒 時間: 2025-3-24 14:08 作者: jungle 時間: 2025-3-24 15:42 作者: intellect 時間: 2025-3-24 19:31 作者: G-spot 時間: 2025-3-25 00:27 作者: sultry 時間: 2025-3-25 03:20
Model Comparisons and Scoring,to be developed and utilized. Chapter . begins by examining some of the newer predictive analytics techniques. It then covers a more in-depth discussion for evaluating different predictive models by evaluating fit statistics. The chapter then concludes with a review of scoring, the process used to a作者: QUAIL 時間: 2025-3-25 09:30
build and analyze a complex analytics model and utilize it to predict future outcomes. The new edition includes chapters on clusters and associations and text mining to support predictive models. An additional case is also included that can be used with each chapter or as a semester project..978-3-030-83072-4978-3-030-83070-0作者: HAIL 時間: 2025-3-25 11:56 作者: RALES 時間: 2025-3-25 17:41 作者: Intentional 時間: 2025-3-25 23:44
What Do Descriptive Statistics Tell Us,e of understanding what if anything will need to be done to the data to prepare it for analysis. There are many statistical tests that can be utilized. This chapter focuses on a review of descriptive statistical analysis that is used to prepare and support predictive analytics. It reviews methods to作者: Hot-Flash 時間: 2025-3-26 03:33 作者: 采納 時間: 2025-3-26 07:03
The Second of the Big 3: Decision Trees,ble to provide an easy method to determine which input variables have an important impact on a target variable. In this chapter, decision trees are defined and then demonstrated to show how they can be used as an important predictive modeling tool. Both classification and regression decision trees w作者: 并入 時間: 2025-3-26 10:48
The Third of the Big 3: Neural Networks, advances in computing speed, memory, and data storage that have enabled their more current widespread use. In this chapter a variety of different neural network architectures will be described. Next an analysis of how to optimize and evaluate neural networks will be presented, followed by using a d作者: Malfunction 時間: 2025-3-26 13:05
Model Comparisons and Scoring,hese are not the only methods available to us. Other methods have been developed, and their use has begun to become more widespread. The predictive analytics landscape has had a significant growth as we see more opportunities to apply these techniques in new and interesting applications. Business pr作者: 裂口 時間: 2025-3-26 20:13
Finding Associations in Data Through Cluster Analysis,s, with each member of the cluster having more in common with members of the same cluster than with members of the other clusters. Cluster analysis is an exploratory data analysis tool which aims at sorting different objects into groups in a way to maximize the degree of association between objects 作者: excrete 時間: 2025-3-26 22:22
Text Analytics: Using Qualitative Data to Support Quantitative Results,olumes of unstructured data. Much of this consists of text data. Let’s consider one example, Twitter. Twitter began operations in mid-2006. Although it took several years to grow into one of the significant social media forces, it is now estimated that they produce over 12 terabytes of data daily (M作者: DEAWL 時間: 2025-3-27 04:06
https://doi.org/10.1007/978-3-030-71644-8 analytics consists primarily of the “Big 3” techniques: regression analysis, decision trees, and neural networks. Although several other techniques, such as random forests and ensemble models, have become increasingly popular in their use, predictive analytics focuses on building and evaluating pre作者: GRAIN 時間: 2025-3-27 06:33 作者: remission 時間: 2025-3-27 11:08 作者: 流行 時間: 2025-3-27 15:00 作者: 注入 時間: 2025-3-27 18:23 作者: 外面 時間: 2025-3-27 22:17 作者: 思想流動 時間: 2025-3-28 02:40 作者: 支形吊燈 時間: 2025-3-28 09:52 作者: 帶來墨水 時間: 2025-3-28 14:14
Studying Discourse Implies Studying Equityolumes of unstructured data. Much of this consists of text data. Let’s consider one example, Twitter. Twitter began operations in mid-2006. Although it took several years to grow into one of the significant social media forces, it is now estimated that they produce over 12 terabytes of data daily (M作者: idiopathic 時間: 2025-3-28 14:41
https://doi.org/10.1007/978-3-030-83070-0Predicative analytics; SAS Enterprise Miner; Neural Networks; Machine Learning; Supervised learning unsu作者: 含糊 時間: 2025-3-28 19:36
978-3-030-83072-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: NAG 時間: 2025-3-28 23:21 作者: 不透氣 時間: 2025-3-29 04:33
http://image.papertrans.cn/b/image/160257.jpg作者: Flawless 時間: 2025-3-29 08:55 作者: 保存 時間: 2025-3-29 14:27 作者: Sciatica 時間: 2025-3-29 16:47
