Summary of model robustness criteria used to ensure analytical validity and generalizability
| Model robustness criterion | Description | Objective |
|---|---|---|
| Train-test split | The data set was divided into 70% training and 30% testing subsets | To evaluate model generalizability and prevent overfitting |
| Stopping criteria |
| To limit model complexity and avoid capturing noise |
| Model robustness criterion | Description | Objective |
|---|---|---|
| Train-test split | The data set was divided into 70% training and 30% testing subsets | To evaluate model generalizability and prevent overfitting |
| Stopping criteria | Minimum node size = 20; Maximum tree depth = 5; Gini index purity improvement threshold = 0.0001 | To limit model complexity and avoid capturing noise |
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