Develop an Optimal Decision Tree Pruning Strategy to Prevent Overfitting

Act as a machine learning engineer specializing in tree-based models and help me develop a pruning strategy for my decision tree that balances accuracy with generalization. **Model and Data Context:** - Task: [CLASSIFICATION/REGRESSION] - Dataset: [NUMBER_OF_SAMPLES] samples, [NUMBER_OF_FEATURES] features - Current tree depth (unpruned): [DEPTH or 'unknown'] - Current training accuracy: [TRAIN_ACCURACY] - Current validation accuracy: [VAL_ACCURACY] - Framework: [SCIKIT-LEARN/SPARK/R/OTHER] - Key concern: [OVERFITTING/INTERPRETABILITY/BOTH] **Please deliver the following:** 1. **Overfitting Diagnosis:** Based on the gap between my training and validation accuracy, quantify the severity of overfitting and explain what it means for production predictions. 2. **Pre-Pruning Strategy:** Recommend specific hyperparameter values for: - `max_depth`: optimal range with justification - `min_samples_split`: recommended value based on my dataset size - `min_samples_leaf`: recommended value and its effect on leaf reliability - `max_features`: when and why to restrict feature consideration - `max_leaf_nodes`: how to set this as a complexity budget Explain the interaction effects between these parameters. 3. **Post-Pruning Strategy (Cost-Complexity Pruning):** Walk me through how to use `ccp_alpha` effectively: - How to generate the cost-complexity pruning path - How to plot effective alpha vs. tree accuracy - How to select the optimal alpha using cross-validation - Provide the complete Python code for this workflow 4. **Pruning Comparison Experiment:** Design a systematic experiment that compares unpruned, pre-pruned, and post-pruned trees. Specify the metrics to track and how to visualize the results. 5. **Interpretability Assessment:** After pruning, explain how to extract and present the simplified decision rules. Recommend the maximum tree depth for human interpretability. 6. **Validation Protocol:** Describe how to validate that the pruned tree genuinely generalizes better, not just performs worse on both sets.