Kaggle's Loan Default Prediction - Imperial College London
This is the R code I used to make my submission to Kaggle's Loan Default Prediction - Imperial College London competition.
My best entry yields 0.45135 on the private LB (0.45185 on the public one), ranking 9 out of 677 participating teams. Using the code here, you can yield similar score. Below, I present a brief description and instruction. The code itself contains lots of comments. So, you'd better see there for details.
It's a two-step approach with a default classifier followed by a loss given default (LGD) regressor. Gradient Boosting Machine (gbm package in R) is used to build both the classification and regression part.
- For the defaulter classifier, I use the golden features revealed by Yasser (thanks a ton!), i.e., f527 and f528, and some other features I found using a GBM. They are stored in the file Defaulter_features.RData in decreasing order with respect to the feature importance returned by a GBM. In specific, I train a GBM with all the raw features and the golden features (use the difference instead, i.e., f274-f528, f274-f527, and f527-f528), then sort it with respect to the feature importance. I have played around with the first 10, 15, and 20 features for classifying defaulter, and found that 15 features probably work best, with a f1-score aroud 0.9488x (sometimes can go to 0.95).
- For the LGD regressor, 107 features are selected using a GBM (similar as we did in selecting features for default model). They are stored in the file LGD_features.RData. In loss regression, I have found that applying logit/logarithm transformation to the loss can help to boost the performance. My best entry is an average of two predictions that use logit and logarithm transformations.
Requirements
You should have packages: data.table, bit64, gbm, and caret installed in R.
Instructions
- Download data from competition website: https://www.kaggle.com/c/loan-default-prediction/
- Download both Defaulter_features.RData and LGD_features.RData
- Set the working directory to the path that contains those data files
- Run loan_default_prediction.R