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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/sas-predictive-modeling-using-logistic-regression
课程评论：没有评论
课程名称：使用SAS进行逻辑回归的预测建模 课程概述：本课程涵盖了使用SAS/STAT软件进行预测建模，重点介绍LOGISTIC过程。课程讨论了变量和交互作用的选择、基于平滑证据权重的分类变量重编码、模型评估、缺失值处理及大数据集的高效技术。您将学习如何使用逻辑回归对个人行为建模，创建效果图和比值比图，处理缺失数据，解决预测变量中的多重共线性问题，评估模型性能并比较模型。 课程大纲： 1. 课程概述与安排：介绍课程内容和学习安排。 2. 理解预测建模：回顾预测建模的基本原理，探索本课程使用的商业场景数据，并讨论建模过程中常见的分析挑战。 3. 拟合模型：研究逻辑回归模型的概念，学习使用LOGISTIC过程拟合逻辑回归模型，评分新案例，并根据过采样调整模型。 4. 输入变量准备（第一部分）：处理预测变量中的常见问题，如缺失值、多级分类预测变量、高冗余预测变量以及与响应变量的非线性关系。 5. 输入变量准备（第二部分）：选择最具预测能力的变量进行建模。 6. 测量模型性能：评估模型的性能，确定最大化利润的分配规则，生成一系列日益复杂的预测模型，并选择最佳模型。 7. SAS认证实践考试 - 使用SAS®9的统计商业分析：回归与建模。 通过本课程，您将掌握使用SAS进行逻辑回归分析的实用技能，从而提高在数据分析和商业决策中的能力。
Name:Course Overview and Logistics
Description:
Name:Understanding Predictive Modeling
Description:In this module, you review the fundamentals of predictive modeling. Then you explore the business scenario data that is used throughout the course. Finally, you learn about common analytical challenges that you might encounter as a modeler.
Name:Fitting the Model
Description:In this module, you investigate the concepts behind the logistic regression model. Then you learn to use the LOGISTIC procedure to fit a logistic regression model. Finally, you learn how to score new cases and adjust the model for oversampling.
Name:Preparing the Input Variables, Part 1
Description:In this module, you learn how to deal with common problems with your predictor variables such as missing values, categorical predictors with many levels, a high number of redundant predictors, and nonlinear relationships with the response variable.
Name:Preparing the Input Variables, Part 2
Description:In this module, you learn how to select the most predictive variables to use in your model.
Name:Measuring Model Performance
Description:In this module, you learn how to assess the performance of your model and how to determine allocation rules that maximize profit. Finally, you learn how to generate a family of increasingly complex predictive models and how to select the best model.
Name:SAS Certification Practice Exam - Statistical Business Analysis Using SAS®9: Regression and Modeling
Description:
This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. You learn to use logistic regression to model an individual's behavior as a function of known inputs, create effect plots and odds ratio plots, handle missing data values, and tackle multicollinearity in your predictors. You also learn to assess model performance and compare models.
