Summary of 2_DecisionTree
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Decision Tree
- criterion: entropy
- max_depth: 4
- num_class: 4
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
16.3 seconds
Metric details
|
0 |
1 |
2 |
3 |
accuracy |
macro avg |
weighted avg |
logloss |
| precision |
0 |
0 |
0.603942 |
0.640371 |
0.618475 |
0.311078 |
0.586603 |
0.807468 |
| recall |
0 |
0 |
0.731171 |
0.570484 |
0.618475 |
0.325414 |
0.618475 |
0.807468 |
| f1-score |
0 |
0 |
0.661494 |
0.603411 |
0.618475 |
0.316226 |
0.598626 |
0.807468 |
| support |
282 |
19 |
2682 |
2419 |
0.618475 |
5402 |
5402 |
0.807468 |
Confusion matrix
|
Predicted as 0 |
Predicted as 1 |
Predicted as 2 |
Predicted as 3 |
| Labeled as 0 |
0 |
0 |
232 |
50 |
| Labeled as 1 |
0 |
0 |
15 |
4 |
| Labeled as 2 |
0 |
0 |
1961 |
721 |
| Labeled as 3 |
0 |
0 |
1039 |
1380 |
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)
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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