標題: Titlebook: Applying Predictive Analytics; Finding Value in Dat Richard V. McCarthy,Mary M. McCarthy,Leila Halawi Textbook 20191st edition Springer Nat [打印本頁] 作者: Deleterious 時間: 2025-3-21 16:16
書目名稱Applying Predictive Analytics影響因子(影響力)
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書目名稱Applying Predictive Analytics網絡公開度
書目名稱Applying Predictive Analytics網絡公開度學科排名
書目名稱Applying Predictive Analytics被引頻次
書目名稱Applying Predictive Analytics被引頻次學科排名
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書目名稱Applying Predictive Analytics年度引用學科排名
書目名稱Applying Predictive Analytics讀者反饋
書目名稱Applying Predictive Analytics讀者反饋學科排名
作者: 法律 時間: 2025-3-21 23:37 作者: Middle-Ear 時間: 2025-3-22 03:45 作者: 顛簸下上 時間: 2025-3-22 06:36 作者: 滋養(yǎng) 時間: 2025-3-22 12:23
Identifying the Determinants of Justice analytics consists primary 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 predi作者: acetylcholine 時間: 2025-3-22 12:57
The Experiences of Pupils Educated Otherwiseand prepare the data for predictive modeling. Most raw data is considered “dirty” or “noisy” because the data may have incomplete information, redundant information, outliers, or errors. Therefore, the data should be analyzed and “cleaned” prior to model development. Chap.?. outlines the entire data作者: ferment 時間: 2025-3-22 20:28 作者: Pulmonary-Veins 時間: 2025-3-23 00:55
https://doi.org/10.1057/9780230277335ive model. Popular regression models include linear regression, logistic regression, principal component regression, and partial least squares. This chapter defines these techniques and when it is appropriate to use the various regression models. Regression assumptions for each type are discussed. E作者: 高度 時間: 2025-3-23 02:31 作者: Mingle 時間: 2025-3-23 06:11 作者: 面包屑 時間: 2025-3-23 10:07
https://doi.org/10.1007/978-981-13-2975-3hese 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 significant growth as we see more opportunities to apply these techniques in new and interesting applications. Business prac作者: 長矛 時間: 2025-3-23 17:30 作者: GLOSS 時間: 2025-3-23 19:07 作者: 植物群 時間: 2025-3-23 23:25 作者: 障礙 時間: 2025-3-24 02:54 作者: fatuity 時間: 2025-3-24 08:40
Predictive Models Using Neural Networks, decision tree to show how to describe a neural network. Finally, multiple neural networks will be applied to the automobile insurance data set to determine which neural network provides the best-fit model.作者: BRAVE 時間: 2025-3-24 12:21
Textbook 20191st editiongnette that provides real-life example of how business analytics have been used in various aspects of organizations to solve issue or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes..作者: 心胸狹窄 時間: 2025-3-24 17:57
The Experiences of Pupils Educated Otherwiseries of data followed by a review of the methods used for preparing the data. After a description of the data classifications and data preparation methods, the process will be reviewed step by step in SAS Enterprise Miner? using the automobile insurance claim data set described in Appendix A.作者: 臭了生氣 時間: 2025-3-24 21:04
https://doi.org/10.1057/9780230277335epwise) 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: clarify 時間: 2025-3-25 02:02 作者: 陰險 時間: 2025-3-25 04:44
,Know Your Data—Data Preparation,ries of data followed by a review of the methods used for preparing the data. After a description of the data classifications and data preparation methods, the process will be reviewed step by step in SAS Enterprise Miner? using the automobile insurance claim data set described in Appendix A.作者: FRAX-tool 時間: 2025-3-25 11:17 作者: 脆弱么 時間: 2025-3-25 13:49
Model Comparisons and Scoring,re not aware that they need it yet. There is what often seems like an infinite amount of data available, and we are no longer constrained by computer processors that were incapable of performing the calculations needed to evaluate a model in a timely manner.作者: obligation 時間: 2025-3-25 17:10 作者: 四牛在彎曲 時間: 2025-3-25 22:32 作者: Filibuster 時間: 2025-3-26 01:34 作者: 小平面 時間: 2025-3-26 06:29
d approach and focus on solving business problems using pred.This textbook presents a practical approach to predictive analytics for classroom learning. It focuses on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the 作者: 拾落穗 時間: 2025-3-26 10:34
Reconsidering What Schools Are For. This chapter focuses on a review of descriptive statistical analysis that is used to prepare and support predictive analytics. It reviews methods to ensure that the data is prepared for analysis as well as methods for combing or reducing variables to improve the results of predictive analytics.作者: Hirsutism 時間: 2025-3-26 16:08
What Do Descriptive Statistics Tell Us,. This chapter focuses on a review of descriptive statistical analysis that is used to prepare and support predictive analytics. It reviews methods to ensure that the data is prepared for analysis as well as methods for combing or reducing variables to improve the results of predictive analytics.作者: Concomitant 時間: 2025-3-26 19:17 作者: anagen 時間: 2025-3-26 22:36
,Know Your Data—Data Preparation,and prepare the data for predictive modeling. Most raw data is considered “dirty” or “noisy” because the data may have incomplete information, redundant information, outliers, or errors. Therefore, the data should be analyzed and “cleaned” prior to model development. Chap.?. outlines the entire data作者: 手銬 時間: 2025-3-27 01:48
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: 削減 時間: 2025-3-27 09:00
Predictive Models Using Regression,ive model. Popular regression models include linear regression, logistic regression, principal component regression, and partial least squares. This chapter defines these techniques and when it is appropriate to use the various regression models. Regression assumptions for each type are discussed. E作者: 有權威 時間: 2025-3-27 12:09
Predictive Models Using 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: amplitude 時間: 2025-3-27 15:43 作者: 保全 時間: 2025-3-27 20:34 作者: myocardium 時間: 2025-3-27 22:32
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