- Credit Risk Modelling - Case Studies
- Classification vs. Regression Models
- Case Study - German Credit - Steps to Build a Predictive Model
- Import Credit Data Set in R
- German Credit Data : Data Preprocessing and Feature Selection in R
- Credit Modelling: Training and Test Data Sets
- Build the Predictive Model
- Logistic Regression Model in R
- Measure Model Performance in R Using ROCR Package
- Create a Confusion Matrix in R
- Credit Risk Modelling - Case Study- Lending Club Data
- Explore Loan Data in R - Loan Grade and Interest Rate
- Credit Risk Modelling - Required R Packages
- Loan Data - Training and Test Data Sets
- Data Cleaning in R - Part 1
- Data Cleaning in R - Part 2
- Data Cleaning in R - Part 3
- Data Cleaning in R - Part 5
- Remove Dimensions By Fitting Logistic Regression
- Create a Function and Prepare Test Data in R
- Building Credit Risk Model
- Credit Risk - Logistic Regression Model in R
- Support Vector Machine (SVM) Model in R
- Random Forest Model in R
- Extreme Gradient Boosting in R
- Predictive Modelling: Averaging Results from Multiple Models
- Predictive Modelling: Comparing Model Results
- How Insurance Companies Calculate Risk

R Programming

## Predictive Modelling: Averaging Results from Multiple Models

Our final model is to combine the result of previous machine learning models and provide a single prediction by averaging probabilities from all previous models.

```
predict_loan_status_ensemble = predict_loan_status_logit +
predict_loan_status_svm +
predict_loan_status_rf +
predict_loan_status_xgb
predict_loan_status_ensemble = predict_loan_status_ensemble / 4
rocCurve_ensemble = roc(response = data_test$loan_status,
predictor = predict_loan_status_ensemble)
auc_curve = auc(rocCurve_ensemble)
plot(rocCurve_ensemble,legacy.axes = TRUE,print.auc = TRUE,col="red",main="ROC(Ensemble Avg.)")
```

```
> rocCurve_ensemble
Call:
roc.default(response = data_test$loan_status, predictor = predict_loan_status_ensemble)
Data: predict_loan_status_ensemble in 5358 controls (data_test$loan_status Default) < 12602 cases (data_test$loan_status Fully.Paid).
Area under the curve: 0.7147
>
```

```
predict_loan_status_label = ifelse(predict_loan_status_ensemble<0.5,"Default","Fully.Paid")
c = confusionMatrix(predict_loan_status_label,data_test$loan_status,positive="Fully.Paid")
table_perf[5,] = c("Ensemble",
round(auc_curve,3),
as.numeric(round(c$overall["Accuracy"],3)),
as.numeric(round(c$byClass["Sensitivity"],3)),
as.numeric(round(c$byClass["Specificity"],3)),
as.numeric(round(c$overall["Kappa"],3))
)
```

We get the following performance:

```
> tail(table_perf,1)
model auc accuracy sensitivity specificity kappa
5 Ensemble 0.715 0.65 0.637 0.68 0.275
>
```

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