Predictive Modeling with Logistic Regression using SAS

本课程是 SAS Statistical Business Analyst 专业证书的一部分

位教师：Marc Huber

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7个模块

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7个模块

深入了解一个主题并学习基础知识。

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在 10 小时一周

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作业

40 项作业

授课语言：英语（English）

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该课程共有7个模块

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.

涵盖的内容

1个视频7篇阅读材料

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.

涵盖的内容

15个视频1篇阅读材料6个作业

15个视频总计34分钟

Overview1分钟
Introduction0分钟
Goals of Predictive Modeling2分钟
Terms for Elements in Predictive Modeling1分钟
Basic Steps of Predictive Modeling3分钟
Applications of Predictive Modeling2分钟
Demonstration Scenario: Target Marketing for a Bank2分钟
Demo: Examining the Code for Generating Descriptive Statistics and Frequency Tables2分钟
Introduction0分钟
Data Challenges6分钟
Analytical Challenges2分钟
Separate Sampling2分钟
Avoiding the Optimism Bias: Honest Assessment2分钟
Splitting the Data for Model Training and Assessment3分钟
Demo: Splitting the Data5分钟

1篇阅读材料总计10分钟

Summary10分钟

6个作业总计75分钟

Practice: Exploring the Bank Data for the Target Marketing Project20分钟
Practice: Exploring the Veterans' Organization Data Used in the Practices20分钟
Question 1.015分钟
Question 1.025分钟
Question 1.035分钟
Practice: Splitting the Data20分钟

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.

涵盖的内容

18个视频1篇阅读材料4个作业

18个视频总计54分钟

Overview1分钟
Introduction0分钟
Understanding the Logistic Regression Model3分钟
Constraining the Posterior Probability Using the Logit Transformation2分钟
Understanding the Fitted Surface1分钟
Interpreting the Model by Calculating the Odds Ratio3分钟
Understanding Logistic Discrimination2分钟
Estimating Unknown Parameters Using Maximum Likelihood Estimation2分钟
Interpreting Concordant, Discordant, and Tied Pairs2分钟
Using PROC LOGISTIC to Fit Logistic Regression Models0分钟
Demo: Fitting a Basic Logistic Regression Model, Part 18分钟
Demo: Fitting a Basic Logistic Regression Model, Part 212分钟
Scoring New Cases0分钟
Demo: Scoring New Cases8分钟
Introduction0分钟
Understanding the Effect of Oversampling1分钟
Understanding the Offset2分钟
Demo: Correcting for Oversampling7分钟

1篇阅读材料总计10分钟

Summary10分钟

4个作业总计60分钟

Fitting the Model Review30分钟
Question 2.015分钟
Question 2.025分钟
Practice: Fitting a Logistic Regression Model20分钟

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.

涵盖的内容

26个视频9个作业

26个视频总计76分钟

Overview1分钟
Introduction0分钟
Reasons for Missing Data3分钟
Complete Case Analysis2分钟
Methods for Imputing Missing Values3分钟
Missing Value Imputation with Missing Value Indicator Variables4分钟
Demo: Imputing Missing Values4分钟
Cluster Imputation2分钟
Introduction0分钟
Problems Caused by Categorical Inputs4分钟
Solutions to Problems Caused by Categorical Inputs1分钟
Linking to Other Data Sets1分钟
Collapsing Categories by Thresholding1分钟
Collapsing Categories by Using Greenacre's Method3分钟
Demo: Collapsing the Levels of a Nominal Input, Part 16分钟
Demo: Collapsing the Levels of a Nominal Input, Part 210分钟
Replacing Categorical Levels by Using Smoothed Weight-of-Evidence Coding3分钟
Demo: Computing the Smoothed Weight of Evidence5分钟
Introduction0分钟
Problem of Redundancy2分钟
Variable Clustering Method1分钟
Understanding Principal Components5分钟
Divisive Clustering4分钟
PROC VARCLUS Syntax1分钟
Selecting a Representative Variable from Each Cluster1分钟
Demo: Reducing Redundancy by Clustering Variables9分钟

9个作业总计105分钟

Question 3.015分钟
Practice: Imputing Missing Values20分钟
Question 3.025分钟
Question 3.035分钟
Question 3.045分钟
Practice: Collapsing the Levels of a Nominal Input20分钟
Practice: Computing the Smoothed Weight of Evidence20分钟
Question 3.055分钟
Practice: Reducing Redundancy by Clustering Variables20分钟

In this module, you learn how to select the most predictive variables to use in your model.

涵盖的内容

23个视频1篇阅读材料12个作业

23个视频总计92分钟

Introduction0分钟
Detecting Nonlinear Relationships4分钟
Demo: Performing Variable Screening, Part 16分钟
Demo: Performing Variable Screening, Part 24分钟
Univariate Binning and Smoothing3分钟
Demo: Creating Empirical Logit Plots10分钟
Remedies for Nonlinear Relationships2分钟
Demo: Accommodating a Nonlinear Relationship, Part 16分钟
Demo: Accommodating a Nonlinear Relationship, Part 28分钟
Introduction0分钟
Specifying a Subset Selection Method in PROC LOGISTIC2分钟
Best-Subsets Selection1分钟
Stepwise Selection3分钟
Backward Elimination2分钟
Scalability of the Subset Selection Methods in PROC LOGISTIC3分钟
Detecting Interactions3分钟
BIC-based Significance Level3分钟
Demo: Detecting Interactions7分钟
Demo: Using Backward Elimination to Subset the Variables4分钟
Demo: Displaying Odds Ratios for Variables Involved in Interactions4分钟
Demo: Creating an Interaction Plot3分钟
Demo: Using the Best-Subsets Selection Method4分钟
Demo: Using Fit Statistics to Select a Model10分钟

1篇阅读材料总计10分钟

Summary of Preparing the Input Variables, Parts 1 and 210分钟

12个作业总计160分钟

Preparing the Input Variables Review30分钟
Question 3.065分钟
Practice: Performing Variable Screening20分钟
Practice: Creating Empirical Logit Plots20分钟
Question 3.075分钟
Question 3.085分钟
Question 3.095分钟
Practice: Using Forward Selection to Detect Interactions20分钟
Question 3.105分钟
Practice: Using Backward Elimination to Subset the Variables20分钟
Question 3.115分钟
Practice: Using Fit Statistics to Select a Model20分钟

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.

涵盖的内容

30个视频1篇阅读材料9个作业

30个视频总计78分钟

Overview1分钟
Introduction0分钟
Fit versus Complexity2分钟
Assessing Models when Target Event Data Is Rare2分钟
Demo: Preparing the Validation Data5分钟
Introduction0分钟
Understanding the Confusion Matrix5分钟
Measuring Performance across Cutoffs by Using the ROC Curve4分钟
Choosing Depth by Using the Gains Chart3分钟
Effects of Oversampled Data on Performance Measures3分钟
Adjusting a Confusion Matrix for Oversampling1分钟
Demo: Measuring Model Performance Based on Commonly-Used Metrics7分钟
Introduction0分钟
Understanding the Effect of Cutoffs on Confusion Matrices1分钟
Understanding the Profit Matrix2分钟
Choosing the Optimal Cutoff by Using the Profit Matrix3分钟
Using the Central Cutoff1分钟
Using Profit to Assess Fit0分钟
Calculating Sampling Weights1分钟
Demo: Using a Profit Matrix to Measure Model Performance6分钟
Introduction0分钟
Plotting Class Separation2分钟
Assessing Overall Predictive Power3分钟
Demo: Using the K-S Statistic to Measure Model Performance2分钟
Introduction0分钟
Comparing ROC Curves of Several Models"2分钟
Demo: Comparing ROC Curves to Measure Model Performance4分钟
Using Macros to Compare Many Models1分钟
Demo: Comparing and Evaluating Many Models, Part 18分钟
Demo: Comparing and Evaluating Many Models, Part 27分钟

1篇阅读材料总计10分钟

Summary10分钟

9个作业总计85分钟

Measuring Model Performance Review30分钟
Question 4.015分钟
Question 4.025分钟
Question 4.035分钟
Practice: Assessing Model Performance20分钟
Question 4.045分钟
Question 4.055分钟
Question 4.065分钟
Question 4.075分钟

涵盖的内容

1篇阅读材料1个应用程序项目

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Marc Huber

SAS

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已于 Apr 10, 2021审阅

Great training sets of problems. Good guidance & teaching.

已于 Jun 14, 2021审阅

Thank you so much to the instructor, Michael J Patetta for teaching this course!

已于 Dec 30, 2022审阅

Very completed and deep knowledge shared with very friendly ways, explained the knowledge very clearly. Also the practices help me to understand the knowledge better.

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