README.md

Summary of 2_Default_Xgboost

<< Go back

Extreme Gradient Boosting (Xgboost)

• n_jobs: -1
• objective: binary:logistic
• eta: 0.075
• max_depth: 6
• min_child_weight: 1
• subsample: 1.0
• colsample_bytree: 1.0
• eval_metric: logloss
• explain_level: 2

Validation

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

Optimized metric

logloss

Training time

6.4 seconds

Metric details

	score	threshold
logloss	0.606966	nan
auc	0.726139	nan
f1	0.697606	0.343999
accuracy	0.666667	0.560322
precision	0.84127	0.816842
recall	1	0.0265062
mcc	0.337439	0.561202

Confusion matrix (at threshold=0.560322)

	Predicted as 0	Predicted as 1
Labeled as 0	630	191
Labeled as 1	347	446

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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)

<< Go back