36 lines
1.8 KiB
Plaintext
36 lines
1.8 KiB
Plaintext
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### Model Performance Summary
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| Model | MAE | RMSE | R² | Within ±1 | Within ±2 | Exact V | Within ±1 V |
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|-------|-----|------|----|-----------|-----------|---------|-------------|
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| Linear Regression | 2.191 | 2.742 | 0.537 | 28.1% | 53.1% | 23.9% | 61.3% |
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| Ridge Regression | 2.191 | 2.742 | 0.537 | 28.1% | 53.1% | 23.9% | 61.3% |
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| Lasso Regression | 2.192 | 2.741 | 0.538 | 27.9% | 53.1% | 23.8% | 61.3% |
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| Random Forest (Tuned) | 1.788 | 2.293 | 0.676 | 36.1% | 64.3% | 30.2% | 70.8% |
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### Key Findings
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1. **Tree-based models remain strongest on this structured feature set.**
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- Random Forest (Tuned) achieves the best overall balance of MAE, RMSE, and grouped V-grade performance.
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- Linear models remain useful baselines but leave clear nonlinear signal unexplained.
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2. **Fine-grained difficulty prediction is meaningfully harder than grouped grade prediction.**
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- On the held-out test set, the best model is within ±1 fine-grained difficulty score 36.1% of the time.
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- The same model is within ±1 grouped V-grade 70.8% of the time.
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3. **This gap is expected and informative.**
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- Small numeric errors often stay inside the same or adjacent V-grade buckets.
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- The model captures broad difficulty bands more reliably than exact score distinctions.
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4. **The project’s main predictive takeaway is practical rather than perfect.**
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- The models are not exact grade replicators.
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- They are reasonably strong at placing climbs into the correct neighborhood of difficulty.
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### Portfolio Interpretation
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From a modelling perspective, this project shows:
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- feature engineering grounded in domain structure,
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- comparison of linear and nonlinear models,
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- honest evaluation on a held-out test set,
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- and the ability to translate raw regression performance into climbing-relevant grouped V-grade metrics.
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