Module 9: Logistic Regression
Logistic Regression is a modelling technique that can be used for classification where the dependent variable values are binary, 1 or 0 as such. The dataset that is used in this module is available under \Bundle\Data\FraudRisk\FraudRisk.csv which contains a set of debit card transactions whereby half of the dataset is a sample of fraudulent transactions, half of the dataset is a sample of legitimate transactions.
To proceed with the subsequent procedures, it is necessary to import the file FraudRisk.csv into R.
Table of contents
- Slides
- Procedure 1: Pivot a Categorical Variable for Regression Analysis
- Procedure 2: Create an Abstraction Deviation Independent Vector
- Procedure 3: Fit a one-way Log Curve on a Plot
- Procedure 4: Forward Stepwise Logistic Regression
- Procedure 5: Recalling a Logistic Regression Model
- Procedure 6: Activating Logistic Regression and Creating a Confusion Matrix
- Procedure 7: Output Logistic Regression Model as Probability
- Procedure 8: Creating a ROC Curve
- Procedure 9: Grading the ROC Performance with AUC