Dataset: energy_efficiency (regression)
Penalty: 0.1
Seed: 3
Best fitness: -2.792080157889667
Final val loss: 2.07498440
Final penalty: 0.09375000
Model saved to: logs/regression/energy_efficiency/models/best_model_penalty_0.1_seed_3.pth

Final architecture & hyperparameters:
  num_layers: 4
  layer_sizes: [10, 3, 5, 9]
  activations: [3, 4, 4, 4]
  dropout_rates: [0.014, 0.105, 0.132, 0.014]
  batch_norms: [0, 0, 1, 1]
  learning_rate: 0.018
  batch_size: 16
  patience: 22
  optimizer_type: 2
  init_type: 0
  l2_penalty: 0.0005

Validation metrics (final):
  mae: 1.1789687871932983
  mse: 2.074984312057495
  rmse: 1.4404805837141628
  r2_score: 0.9803622364997864
  mape: 7.092997431755066
  residual_std: 1.4110575914382935
  prediction_std: 9.653945922851562
  target_std: 10.279247283935547
  normalized_mae: 0.03282207258608876
  normalized_rmse: 0.04010246818329552
  num_samples: 115
  target_range: 35.91999816894531
  prediction_range: 33.566871643066406

Test metrics (final):
  mae: 1.1712757349014282
  mse: 2.102794885635376
  rmse: 1.450101681136663
  r2_score: 0.9802165031433105
  mape: 6.134389713406563
  residual_std: 1.448189616203308
  prediction_std: 9.694202423095703
  target_std: 10.309714317321777
  normalized_mae: 0.03363801680548352
  normalized_rmse: 0.04164565461935405
  num_samples: 116
  target_range: 34.81999969482422
  prediction_range: 32.7470703125
