Summary of 2_DecisionTree
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Decision Tree
- criterion: entropy
- max_depth: 4
- num_class: 5
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
15.6 seconds
Metric details
|
0 |
1 |
2 |
3 |
4 |
accuracy |
macro avg |
weighted avg |
logloss |
| precision |
0.923077 |
0.375 |
1 |
0.392857 |
0.5 |
0.56 |
0.638187 |
0.639953 |
0.919744 |
| recall |
0.765957 |
0.54 |
0.413793 |
0.323529 |
0.8 |
0.56 |
0.568656 |
0.56 |
0.919744 |
| f1-score |
0.837209 |
0.442623 |
0.585366 |
0.354839 |
0.615385 |
0.56 |
0.567084 |
0.570005 |
0.919744 |
| support |
47 |
50 |
29 |
34 |
15 |
0.56 |
175 |
175 |
0.919744 |
Confusion matrix
|
Predicted as 0 |
Predicted as 1 |
Predicted as 2 |
Predicted as 3 |
Predicted as 4 |
| Labeled as 0 |
36 |
5 |
0 |
6 |
0 |
| Labeled as 1 |
0 |
27 |
0 |
11 |
12 |
| Labeled as 2 |
1 |
16 |
12 |
0 |
0 |
| Labeled as 3 |
1 |
22 |
0 |
11 |
0 |
| Labeled as 4 |
1 |
2 |
0 |
0 |
12 |
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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