34 lines
1.3 KiB
Plaintext
34 lines
1.3 KiB
Plaintext
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### Neural Network Model Summary
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**Architecture:**
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- Input: 119 features
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- Hidden layers: [256, 128, 64]
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- Dropout rate: 0.2
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- Total parameters: 72,833
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**Training:**
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- Optimizer: Adam (lr=0.001)
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- Early stopping: 25 epochs patience
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- Best epoch: 121
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**Test Set Performance:**
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- MAE: 1.270
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- RMSE: 1.643
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- R²: 0.834
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- Accuracy within ±1 grade: 49.0%
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- Accuracy within ±2 grades: 80.2%
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- Exact grouped V-grade accuracy: 39.2%
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- Accuracy within ±1 V-grade: 84.3%
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- Accuracy within ±2 V-grades: 96.8%
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**Key Findings:**
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1. The neural network is competitive, but not clearly stronger than the best tree-based baseline.
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2. Fine-grained score prediction remains harder than grouped grade prediction.
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3. The grouped V-grade metrics show that the model captures broader difficulty bands more reliably than exact score labels.
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4. This makes the neural network useful as a comparison model, and potentially valuable in an ensemble.
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**Portfolio Interpretation:**
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This deep learning notebook extends the classical modelling pipeline by testing whether a neural architecture can improve prediction quality on engineered climbing features.
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The main result is not that deep learning wins outright, but that it provides a meaningful benchmark and helps clarify where model complexity does and does not add value.
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