Credit Risk – Logistic Regression Model in R

To build our first model, we will tune Logistic Regression to our training dataset.

First we set the seed (to any number. we have chosen 100) so that we can reproduce our results.

Then we create a downsampled dataset called samp which contains an equal number of Default and Fully Paid loans. We can use the table() function to check that the downsampling is done correctly.

We will now choose a small set of data for tuning the model.

We will use the functions available in the caret package to train the model.

Step 1: We setup the control parameter to train with the 3-fold cross validation (cv)

Step 2: We train the classification model on the data

Advanced Notes

We use Elastic Net regularization, which comprises of Ridge and Lasso regularization, with cross-validation to prevent overfitting. Our goal is maximizing AUC. Learn more about Regularization here.

Due to limited computation resource, we run model tuning on small data and fixed lambda parameter. We use small fold: 3-fold cross validation. We then refit the best model with the whole data.

(Note: we put the final tuning result here instead of running through the whole process. We disable the execution of tuning code although readers can enable it back by setting eval = TRUE )

Look Inside glmTuned

We can now examine the output of the generated model.

The best penalty parameter is alpha = 1 (more weight on Ridge) with fixed shrinking lambda = 0.01. We use this parameter to retrain the whole sample.

The finalized Logistic Regression model is applied to the test loan data. We look at ROC graph and AUC. We also set probability prediction cutoff at 50% (noted that the higher this value is, the more likely the loan is Fully Paid) and collect some performance metrics for a later comparison.

ROC and AUC

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