Dataset: energy_efficiency (regression)
Penalty: 0.3
Seed: 42
Best fitness: -0.04911731216642592
Final val loss: 0.04596645
Final penalty: 0.00611111
Model saved to: exp_2/logs/regression/energy_efficiency/models/best_model_penalty_0.3_seed_42.pth

Final architecture & hyperparameters:
  num_layers: 3
  layer_sizes: [1, 1, 1]
  activations: [3, 3, 1]
  dropout_rates: [0.016, 0.016, 0.016]
  batch_norms: [1, 1, 1]
  learning_rate: 0.0253
  batch_size: 16
  patience: 21
  optimizer_type: 2
  init_type: 2
  l2_penalty: 0.0002

Validation metrics (final):
  mae: 2.121654987335205
  mse: 9.791580200195312
  rmse: 3.129150076329883
  r2_score: 0.9042060971260071
  mape: 9.592444449663162
  residual_std: 3.121215581893921
  prediction_std: 9.223881721496582
  target_std: 10.110147476196289
  normalized_mae: 0.05785805488316073
  normalized_rmse: 0.08533269449305472
  num_samples: 115
  target_range: 36.67000198364258
  prediction_range: 27.897586822509766

Test metrics (final):
  mae: 1.8913406133651733
  mse: 7.481155872344971
  rmse: 2.7351701724655033
  r2_score: 0.9236248135566711
  mape: 9.486354887485504
  residual_std: 2.6985199451446533
  prediction_std: 9.018101692199707
  target_std: 9.89710521697998
  normalized_mae: 0.05169009580426047
  normalized_rmse: 0.07475184916805894
  num_samples: 116
  target_range: 36.59000015258789
  prediction_range: 27.561323165893555
