Summary of 4_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- objective: multi:softprob
- eval_metric: mlogloss
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- num_class: 5
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
9.0 seconds
Metric details
|
0 |
1 |
2 |
3 |
4 |
accuracy |
macro avg |
weighted avg |
logloss |
| precision |
0.886364 |
0.5 |
0.62963 |
0.485714 |
0.526316 |
0.617143 |
0.605605 |
0.624728 |
0.796518 |
| recall |
0.829787 |
0.5 |
0.586207 |
0.5 |
0.666667 |
0.617143 |
0.616532 |
0.617143 |
0.796518 |
| f1-score |
0.857143 |
0.5 |
0.607143 |
0.492754 |
0.588235 |
0.617143 |
0.609055 |
0.619829 |
0.796518 |
| support |
47 |
50 |
29 |
34 |
15 |
0.617143 |
175 |
175 |
0.796518 |
Confusion matrix
|
Predicted as 0 |
Predicted as 1 |
Predicted as 2 |
Predicted as 3 |
Predicted as 4 |
| Labeled as 0 |
39 |
4 |
0 |
4 |
0 |
| Labeled as 1 |
1 |
25 |
2 |
13 |
9 |
| Labeled as 2 |
3 |
8 |
17 |
1 |
0 |
| Labeled as 3 |
1 |
8 |
8 |
17 |
0 |
| Labeled as 4 |
0 |
5 |
0 |
0 |
10 |
Learning curves
Permutation-based Importance
SHAP Importance
SHAP Dependence plots
Dependence 0 (Fold 1)
Dependence 1 (Fold 1)
Dependence 2 (Fold 1)
Dependence 3 (Fold 1)
Dependence 4 (Fold 1)
SHAP Decision plots
Worst decisions for selected sample 1 (Fold 1)
Worst decisions for selected sample 2 (Fold 1)
Worst decisions for selected sample 3 (Fold 1)
Worst decisions for selected sample 4 (Fold 1)
Best decisions for selected sample 1 (Fold 1)
Best decisions for selected sample 2 (Fold 1)
Best decisions for selected sample 3 (Fold 1)
Best decisions for selected sample 4 (Fold 1)
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