README.md

Summary of 5_Default_Xgboost

Extreme Gradient Boosting (Xgboost)

• objective: binary:logistic
• eval_metric: logloss
• eta: 0.1
• max_depth: 6
• min_child_weight: 1
• subsample: 1.0
• colsample_bytree: 1.0
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

5.2 seconds

Metric details

	score	threshold
logloss	0.273109	nan
auc	0.931203	nan
f1	0.738846	0.382176
accuracy	0.877611	0.473314
precision	0.983229	0.858185
recall	1	9.98914e-05
mcc	0.655066	0.418355

Confusion matrix (at threshold=0.382176)

	Predicted as negative	Predicted as positive
Labeled as negative	4491	453
Labeled as positive	384	1184

Learning curves

Permutation-based Importance

SHAP Importance

SHAP Dependence plots

Dependence (Fold #1)

SHAP Decision plots

Top-10 Worst decisions for class 0 (Fold #1)

Top-10 Best decisions for class 0 (Fold #1)

Top-10 Worst decisions for class 1 (Fold #1)

Top-10 Best decisions for class 1 (Fold #1)