output.txt 2.7 KB

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  1. mu = 12.02 and sigma = 0.40
  2. Total Percent
  3. PoolQC 2908 0.996915
  4. MiscFeature 2812 0.964004
  5. Alley 2719 0.932122
  6. Fence 2346 0.804251
  7. FireplaceQu 1420 0.486802
  8. LotFrontage 486 0.166610
  9. GarageCond 159 0.054508
  10. GarageQual 159 0.054508
  11. GarageYrBlt 159 0.054508
  12. GarageFinish 159 0.054508
  13. GarageType 157 0.053822
  14. BsmtCond 82 0.028111
  15. BsmtExposure 82 0.028111
  16. BsmtQual 81 0.027768
  17. BsmtFinType2 80 0.027425
  18. BsmtFinType1 79 0.027083
  19. MasVnrType 24 0.008228
  20. MasVnrArea 23 0.007885
  21. MSZoning 4 0.001371
  22. BsmtHalfBath 2 0.000686
  23. Functional 2 0.000686
  24. BsmtFullBath 2 0.000686
  25. BsmtFinSF2 1 0.000343
  26. BsmtUnfSF 1 0.000343
  27. BsmtFinSF1 1 0.000343
  28. Exterior2nd 1 0.000343
  29. TotalBsmtSF 1 0.000343
  30. Exterior1st 1 0.000343
  31. SaleType 1 0.000343
  32. Electrical 1 0.000343
  33. KitchenQual 1 0.000343
  34. GarageCars 1 0.000343
  35. GarageArea 1 0.000343
  36. ElasticNetCV(alphas=[0.0001, 0.0005, 0.001, 0.01, 0.1, 1, 10], copy_X=True,
  37. cv=None, eps=0.001, fit_intercept=True,
  38. l1_ratio=[0.01, 0.1, 0.5, 0.9, 0.99], max_iter=5000, n_alphas=100,
  39. n_jobs=1, normalize=False, positive=False, precompute='auto',
  40. random_state=None, selection='cyclic', tol=0.0001, verbose=0)
  41. R2: 0.9375476904713028
  42. RMSE: 0.09510356919204585
  43. RMSLE: 0.007404587030567225
  44. Test
  45. R2: 0.9241970969211174
  46. RMSE: 0.10801284037386255
  47. RMSLE: 0.008503460503189928
  48. Accuracy: 0.92 (+/- 0.02)
  49. GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
  50. learning_rate=0.05, loss='huber', max_depth=3,
  51. max_features='sqrt', max_leaf_nodes=None,
  52. min_impurity_decrease=0.0, min_impurity_split=None,
  53. min_samples_leaf=15, min_samples_split=10,
  54. min_weight_fraction_leaf=0.0, n_estimators=3000,
  55. presort='auto', random_state=None, subsample=1.0, verbose=0,
  56. warm_start=False)
  57. R2: 0.9712365659486509
  58. RMSE: 0.06489702799840345
  59. RMSLE: 0.005148721510078062
  60. Test
  61. R2: 0.8856412136806209
  62. RMSE: 0.12677478597418662
  63. RMSLE: 0.010065803355386425
  64. Accuracy: 0.91 (+/- 0.03)
  65. XGBRegressor(base_score=0.5, colsample_bylevel=1, colsample_bytree=1, gamma=0,
  66. learning_rate=0.05, max_delta_step=0, max_depth=3,
  67. min_child_weight=1, missing=None, n_estimators=3000, nthread=-1,
  68. objective='reg:linear', reg_alpha=0, reg_lambda=1,
  69. scale_pos_weight=1, seed=0, silent=True, subsample=1)
  70. R2: 0.998102033663671
  71. RMSE: 0.01716734991141679
  72. RMSLE: 0.0013246240985368375
  73. Test
  74. R2: 0.8912268252304474
  75. RMSE: 0.12701108893000432
  76. RMSLE: 0.010024565631835718
  77. Accuracy: 0.91 (+/- 0.04)