Your question is Evaluate Cross-Validation Impact on Model Performance. Take a moment with it on the right.
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HomePricePredictor is a machine learning model designed to predict housing prices based on various features such as location, size, and amenities. After initial deployment, the model showed inconsistent performance across different datasets, raising concerns about its reliability. The data science team implemented k-fold cross-validation to assess the model's generalization capabilities better.
| Metric | Initial Evaluation | After Cross-Validation | Change |
|---|---|---|---|
| RMSE | $25,000 | $18,500 | -26.0% |
| R² | 0.75 | 0.85 | +13.3% |
| MAE | $20,000 | $15,000 | -25.0% |
| Training Time | 2 hours | 3 hours | +50.0% |
While cross-validation improved the model's RMSE and R² significantly, the increased training time raises questions about efficiency. The team needs to understand the trade-offs between model performance and computational cost.