Predictive Modeling Using Logistic Regression
Predictive Modeling Using Logistic Regression Course Details:
In this course, you will learn about predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. You will also learn about 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.
SAS Statistical Business Analysis Using SAS 9: Regression and Modeling
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1. Predictive Modeling
- Business applications
- Analytical challenges
2. Fitting the Model
- Parameter estimation
- Adjustments for oversampling
3. Preparing the Input Variables
- Missing values
- Categorical inputs
- Variable clustering
- Variable screening
- Subset selection
4. Classifier Performance
- ROC curves and Lift charts
- Optimal cutoffs
- K-S statistic
- c statistic
- Evaluating a series of models
*Please Note: Course Outline is subject to change without notice. Exact course outline will be provided at time of registration.
Exercises or hands-on workshops are included with most SAS courses.
- Modelers, analysts and statisticians who need to build predictive models, particularly models from the banking, financial services, direct marketing, insurance and telecommunications industries