Predictive Modeling with Logistic Regression using SAS

1 Videos

• Overview

2 Assignment

• Practice: Exploring the Bank Data for the Target Marketing Project
• Practice: Exploring the Veterans' Organization Data Used in the Practices

7 Videos

• Introduction
• Goals of Predictive Modeling
• Terms for Elements in Predictive Modeling
• Basic Steps of Predictive Modeling
• Applications of Predictive Modeling
• Demonstration Scenario: Target Marketing for a Bank
• Demo: Examining the Code for Generating Descriptive Statistics and Frequency Tables

4 Assignment

• Question 1.01
• Question 1.02
• Question 1.03
• Practice: Splitting the Data

7 Videos

• Introduction
• Data Challenges
• Analytical Challenges
• Separate Sampling
• Avoiding the Optimism Bias: Honest Assessment
• Splitting the Data for Model Training and Assessment
• Demo: Splitting the Data

1 Readings

• Summary

1 Videos

• Overview

2 Assignment

• Question 2.01
• Question 2.02

13 Videos

• Introduction
• Understanding the Logistic Regression Model
• Constraining the Posterior Probability Using the Logit Transformation
• Understanding the Fitted Surface
• Interpreting the Model by Calculating the Odds Ratio
• Understanding Logistic Discrimination
• Estimating Unknown Parameters Using Maximum Likelihood Estimation
• Interpreting Concordant, Discordant, and Tied Pairs
• Using PROC LOGISTIC to Fit Logistic Regression Models
• Demo: Fitting a Basic Logistic Regression Model, Part 1
• Demo: Fitting a Basic Logistic Regression Model, Part 2
• Scoring New Cases
• Demo: Scoring New Cases

1 Assignment

• Practice: Fitting a Logistic Regression Model

4 Videos

• Introduction
• Understanding the Effect of Oversampling
• Understanding the Offset
• Demo: Correcting for Oversampling

1 Assignment

• Fitting the Model Review

1 Readings

• Summary

1 Videos

• Overview

2 Assignment

• Question 3.01
• Practice: Imputing Missing Values

7 Videos

• Introduction
• Reasons for Missing Data
• Complete Case Analysis
• Methods for Imputing Missing Values
• Missing Value Imputation with Missing Value Indicator Variables
• Demo: Imputing Missing Values
• Cluster Imputation

5 Assignment

• Question 3.02
• Question 3.03
• Question 3.04
• Practice: Collapsing the Levels of a Nominal Input
• Practice: Computing the Smoothed Weight of Evidence

10 Videos

• Introduction
• Problems Caused by Categorical Inputs
• Solutions to Problems Caused by Categorical Inputs
• Linking to Other Data Sets
• Collapsing Categories by Thresholding
• Collapsing Categories by Using Greenacre's Method
• Demo: Collapsing the Levels of a Nominal Input, Part 1
• Demo: Collapsing the Levels of a Nominal Input, Part 2
• Replacing Categorical Levels by Using Smoothed Weight-of-Evidence Coding
• Demo: Computing the Smoothed Weight of Evidence

2 Assignment

• Question 3.05
• Practice: Reducing Redundancy by Clustering Variables

8 Videos

• Introduction
• Problem of Redundancy
• Variable Clustering Method
• Understanding Principal Components
• Divisive Clustering
• PROC VARCLUS Syntax
• Selecting a Representative Variable from Each Cluster
• Demo: Reducing Redundancy by Clustering Variables

5 Assignment

• Question 3.06
• Practice: Performing Variable Screening
• Practice: Creating Empirical Logit Plots
• Question 3.07
• Question 3.08

9 Videos

• Introduction
• Detecting Nonlinear Relationships
• Demo: Performing Variable Screening, Part 1
• Demo: Performing Variable Screening, Part 2
• Univariate Binning and Smoothing
• Demo: Creating Empirical Logit Plots
• Remedies for Nonlinear Relationships
• Demo: Accommodating a Nonlinear Relationship, Part 1
• Demo: Accommodating a Nonlinear Relationship, Part 2

6 Assignment

• Question 3.09
• Practice: Using Forward Selection to Detect Interactions
• Question 3.10
• Practice: Using Backward Elimination to Subset the Variables
• Question 3.11
• Practice: Using Fit Statistics to Select a Model

14 Videos

• Introduction
• Specifying a Subset Selection Method in PROC LOGISTIC
• Best-Subsets Selection
• Stepwise Selection
• Backward Elimination
• Scalability of the Subset Selection Methods in PROC LOGISTIC
• Detecting Interactions
• BIC-based Significance Level
• Demo: Detecting Interactions
• Demo: Using Backward Elimination to Subset the Variables
• Demo: Displaying Odds Ratios for Variables Involved in Interactions
• Demo: Creating an Interaction Plot
• Demo: Using the Best-Subsets Selection Method
• Demo: Using Fit Statistics to Select a Model

1 Assignment

• Preparing the Input Variables Review

1 Readings

• Summary of Preparing the Input Variables, Parts 1 and 2

1 Videos

• Overview

1 Assignment

• Question 4.01

4 Videos

• Introduction
• Fit versus Complexity
• Assessing Models when Target Event Data Is Rare
• Demo: Preparing the Validation Data

3 Assignment

• Question 4.02
• Question 4.03
• Practice: Assessing Model Performance

7 Videos

• Introduction
• Understanding the Confusion Matrix
• Measuring Performance across Cutoffs by Using the ROC Curve
• Choosing Depth by Using the Gains Chart
• Effects of Oversampled Data on Performance Measures
• Adjusting a Confusion Matrix for Oversampling
• Demo: Measuring Model Performance Based on Commonly-Used Metrics

2 Assignment

• Question 4.04
• Question 4.05

8 Videos

• Introduction
• Understanding the Effect of Cutoffs on Confusion Matrices
• Understanding the Profit Matrix
• Choosing the Optimal Cutoff by Using the Profit Matrix
• Using the Central Cutoff
• Using Profit to Assess Fit
• Calculating Sampling Weights
• Demo: Using a Profit Matrix to Measure Model Performance

1 Assignment

• Question 4.06

4 Videos

• Introduction
• Plotting Class Separation
• Assessing Overall Predictive Power
• Demo: Using the K-S Statistic to Measure Model Performance

1 Assignment

• Question 4.07

6 Videos

• Introduction
• Comparing ROC Curves of Several Models"
• Demo: Comparing ROC Curves to Measure Model Performance
• Using Macros to Compare Many Models
• Demo: Comparing and Evaluating Many Models, Part 1
• Demo: Comparing and Evaluating Many Models, Part 2

1 Assignment

• Measuring Model Performance Review

1 Readings

• Summary

1 External Tool

• Access the Practice Exam

1 Readings

• About the Certification Exam