Machine Learning Models
- Decision Tree Model
RMSE (Root Mean Square Error): 118.0916
Analysis: This RMSE value indicates that the average error in the predictions made by the decision tree model is about 118 students. The decision tree model, being relatively simple, might not have captured the complex interactions and nuances in the data. This model is useful for its interpretability but less so for high accuracy in predictions.
- XGBoost Model
RMSE: 15.87645
Analysis: The XGBoost model significantly outperformed the decision tree model with a much lower RMSE. This improvement suggests that the XGBoost model was able to capture more complex relationships in the data. An average prediction error of approximately 16 students is relatively small, indicating a high level of accuracy. This model is suitable for scenarios where prediction accuracy is paramount.
- Random Forest Model RMSE: 56.0548
Analysis: The random forest model, another ensemble learning method, also performed better than the decision tree but not as well as XGBoost. The RMSE of around 56 students suggests a moderate level of prediction error. This model strikes a balance between accuracy and interpretability and can be useful when one needs a more robust model than a decision tree without getting into the complexity of XGBoost.
Overall Implications: Model Selection for Policy and Planning: The choice of model can significantly impact the accuracy of predictions for school enrollment. XGBoost, with its high accuracy, is particularly useful for detailed and precise planning and allocation of resources. Interpretability vs. Accuracy Trade-off: There’s a clear trade-off between model simplicity (and thus interpretability) and prediction accuracy. Decision trees are easy to understand but less accurate, while XGBoost offers high accuracy but is more complex. Predictive Power and Application: The models, especially XGBoost, can be valuable tools for forecasting school enrollments, which is crucial for educational planning, resource allocation, and policy-making in New York City.
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dbl (43): year, total_enrollment, grade_pk_(half_day_&_full_day), grade_k, g...
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