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Credit Risk Modeling Techniques in R by Arjun Khosla is a document available to read on EtoBox.

Here are the key steps for building a logistic regression model in R: 1. Split the data into training and test sets. This allows you to train the model on a portion of the data and validate it on held-out data. 2. Fit a null model with only the intercept term. This serves as a baseline to compare subsequent models against. 3. Fit models adding variables one by one and check their significance levels. Remove non-significant variables. 4. Compare the residual deviance and null deviance of each model. L

Author

Arjun Khosla

Language

EN