Course: Predictive Modeling with Logistic Regression using SAS
Modules:
Meet the Instructor
Overview, 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, 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
Overview, 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, Introduction, Understanding the Effect of Oversampling, Understanding the Offset, Demo: Correcting for Oversampling
Overview, 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, 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, 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
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, 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
Overview, Introduction, Fit versus Complexity, Assessing Models when Target Event Data Is Rare, Demo: Preparing the Validation Data, 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, 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, Introduction, Plotting Class Separation, Assessing Overall Predictive Power, Demo: Using the K-S Statistic to Measure Model Performance, 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
About the Certification Exam