A hands-on demo of Decision Tree classification with pre-pruning and post-pruning techniques to prevent overfitting and boost model reliability.
- Pre-Pruning (Early Stopping) β
- Post-Pruning (Cost Complexity Pruning) βοΈ
- Hyperparameter tuning with GridSearchCV βοΈ
- Performance evaluation with Accuracy Score & Classification Report π
- Clone the repository:
https://github.com/Sidra-009/decision-tree-classification.git
βοΈ Key Takeaway
Pruning keeps models efficient, reliable & generalizable across datasets.