# Load required libraries library (tidyverse)
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✔ dplyr 1.1.4 ✔ readr 2.1.5
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✔ ggplot2 3.5.0 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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Loading required package: lattice
Attaching package: 'caret'
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library (e1071)library (randomForest)
randomForest 4.7-1.1
Type rfNews() to see new features/changes/bug fixes.
Attaching package: 'randomForest'
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Attaching package: 'xgboost'
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library (lightgbm)library (ROCR)library (ggplot2)library (data.table)
Attaching package: 'data.table'
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# EDA: Visualizations ggplot (df_credit, aes (x = Age)) + geom_histogram (binwidth = 5 ) + facet_wrap (~ Risk) + ggtitle ("Age Distribution by Risk" )ggplot (df_credit, aes (x = Sex, fill = Risk)) + geom_bar (position = "dodge" ) + ggtitle ("Sex Distribution by Risk" )ggplot (df_credit, aes (x = Job, fill = Risk)) + geom_bar (position = "dodge" ) + ggtitle ("Job Distribution by Risk" )ggplot (df_credit, aes (x = Housing, fill = Risk)) + geom_bar (position = "dodge" ) + ggtitle ("Housing Distribution by Risk" )ggplot (df_credit, aes (x = Saving.accounts, fill = Risk)) + geom_bar (position = "dodge" ) + ggtitle ("Saving Accounts Distribution by Risk" )ggplot (df_credit, aes (x = Checking.account, fill = Risk)) + geom_bar (position = "dodge" ) + ggtitle ("Checking Account Distribution by Risk" )ggplot (df_credit, aes (x = Credit.amount)) + geom_histogram (binwidth = 500 ) + facet_wrap (~ Risk) + ggtitle ("Credit Amount Distribution by Risk" )ggplot (df_credit, aes (x = Duration)) + geom_histogram (binwidth = 5 ) + facet_wrap (~ Risk) + ggtitle ("Duration Distribution by Risk" )ggplot (df_credit, aes (x = Purpose, fill = Risk)) + geom_bar (position = "dodge" ) + ggtitle ("Purpose Distribution by Risk" )
# Evaluate models on test data <- list (glm = model_glm, rf = model_rf, svm = model_svm, xgb = model_xgb)<- resamples (models)summary (results)
Call:
summary.resamples(object = results)
Models: glm, rf, svm, xgb
Number of resamples: 5
ROC
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
glm 0.6145833 0.7148438 0.7795759 0.7558036 0.8132440 0.8567708 0
rf 0.6433222 0.7472098 0.7650670 0.7521577 0.7936198 0.8115699 0
svm 0.6396949 0.7358631 0.7979911 0.7567336 0.8007812 0.8093378 0
xgb 0.6343006 0.7510231 0.7711124 0.7664807 0.8314732 0.8444940 0
Sens
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
glm 0.3750000 0.3750000 0.4375000 0.4166667 0.4375000 0.4583333 0
rf 0.3541667 0.3750000 0.3958333 0.4250000 0.4791667 0.5208333 0
svm 0.2500000 0.3541667 0.3541667 0.3625000 0.3958333 0.4583333 0
xgb 0.2916667 0.2916667 0.3750000 0.3666667 0.4166667 0.4583333 0
Spec
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
glm 0.8125000 0.8482143 0.8571429 0.8750000 0.9107143 0.9464286 0
rf 0.8035714 0.8392857 0.8750000 0.8678571 0.8839286 0.9375000 0
svm 0.8392857 0.8839286 0.8839286 0.9035714 0.9464286 0.9642857 0
xgb 0.8392857 0.8392857 0.8660714 0.8785714 0.8839286 0.9642857 0
eXtreme Gradient Boosting
800 samples
9 predictor
2 classes: 'bad', 'good'
No pre-processing
Resampling: Cross-Validated (5 fold)
Summary of sample sizes: 640, 640, 640, 640, 640
Resampling results across tuning parameters:
eta max_depth colsample_bytree subsample nrounds ROC Sens
0.3 1 0.6 0.500 50 0.7566592 0.3541667
0.3 1 0.6 0.500 100 0.7496466 0.4166667
0.3 1 0.6 0.500 150 0.7435454 0.4041667
0.3 1 0.6 0.500 200 0.7321615 0.4041667
0.3 1 0.6 0.500 250 0.7286458 0.3958333
0.3 1 0.6 0.625 50 0.7588542 0.3458333
0.3 1 0.6 0.625 100 0.7446615 0.3708333
0.3 1 0.6 0.625 150 0.7414249 0.4000000
0.3 1 0.6 0.625 200 0.7358073 0.3958333
0.3 1 0.6 0.625 250 0.7291481 0.3916667
0.3 1 0.6 0.750 50 0.7607515 0.3458333
0.3 1 0.6 0.750 100 0.7505208 0.4000000
0.3 1 0.6 0.750 150 0.7491629 0.4000000
0.3 1 0.6 0.750 200 0.7443266 0.4208333
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0.3 1 0.6 0.875 50 0.7631510 0.3625000
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0.3 1 0.6 1.000 250 0.7493304 0.3875000
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0.3 1 0.8 0.500 100 0.7481585 0.3875000
0.3 1 0.8 0.500 150 0.7463356 0.4208333
0.3 1 0.8 0.500 200 0.7412016 0.4041667
0.3 1 0.8 0.500 250 0.7282180 0.4500000
0.3 1 0.8 0.625 50 0.7664807 0.3666667
0.3 1 0.8 0.625 100 0.7568266 0.4166667
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0.8375000
0.8250000
0.8321429
0.8339286
0.8535714
0.8392857
0.8285714
0.8303571
0.8250000
0.8321429
0.8321429
0.8125000
0.8178571
0.8214286
0.8392857
0.8232143
0.8214286
0.8250000
0.8196429
0.8428571
0.8267857
0.8214286
0.8303571
0.8303571
0.8196429
0.8267857
0.8142857
0.8214286
0.8214286
0.8571429
0.8392857
0.8410714
0.8339286
0.8410714
Tuning parameter 'gamma' was held constant at a value of 0
Tuning
parameter 'min_child_weight' was held constant at a value of 1
ROC was used to select the optimal model using the largest value.
The final values used for the model were nrounds = 50, max_depth = 1, eta
= 0.3, gamma = 0, colsample_bytree = 0.8, min_child_weight = 1 and subsample
= 0.625.
# ROC Curve <- prediction (probabilities[, 2 ], df_test$ Risk)<- performance (pred, "tpr" , "fpr" )plot (perf, col = "blue" , main = "ROC Curve" )abline (a = 0 , b = 1 , col = "red" , lty = 2 )
