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output.txt

output.txt 2.4 KB
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  1. 

  2.  mu = 12.02 and sigma = 0.40
    3. 

  3. 

  4.               Total   Percent
    5. 

  5. PoolQC         2908  0.996915
    6. 

  6. MiscFeature    2812  0.964004
    7. 

  7. Alley          2719  0.932122
    8. 

  8. Fence          2346  0.804251
    9. 

  9. FireplaceQu    1420  0.486802
    10. 

  10. LotFrontage     486  0.166610
    11. 

  11. GarageCond      159  0.054508
    12. 

  12. GarageQual      159  0.054508
    13. 

  13. GarageYrBlt     159  0.054508
    14. 

  14. GarageFinish    159  0.054508
    15. 

  15. GarageType      157  0.053822
    16. 

  16. BsmtCond         82  0.028111
    17. 

  17. BsmtExposure     82  0.028111
    18. 

  18. BsmtQual         81  0.027768
    19. 

  19. BsmtFinType2     80  0.027425
    20. 

  20. BsmtFinType1     79  0.027083
    21. 

  21. MasVnrType       24  0.008228
    22. 

  22. MasVnrArea       23  0.007885
    23. 

  23. MSZoning          4  0.001371
    24. 

  24. BsmtHalfBath      2  0.000686
    25. 

  25. Utilities         2  0.000686
    26. 

  26. Functional        2  0.000686
    27. 

  27. BsmtFullBath      2  0.000686
    28. 

  28. BsmtFinSF2        1  0.000343
    29. 

  29. BsmtFinSF1        1  0.000343
    30. 

  30. Exterior2nd       1  0.000343
    31. 

  31. BsmtUnfSF         1  0.000343
    32. 

  32. TotalBsmtSF       1  0.000343
    33. 

  33. Exterior1st       1  0.000343
    34. 

  34. SaleType          1  0.000343
    35. 

  35. Electrical        1  0.000343
    36. 

  36. KitchenQual       1  0.000343
    37. 

  37. GarageArea        1  0.000343
    38. 

  38. GarageCars        1  0.000343
    39. 

  39. 

  40. Skew in numerical features:
    41. 

  41. There are 81 skewed numerical features to Box Cox transform
    42. 

  42. 

  43. Pipeline(memory=None,
    44. 

  44.      steps=[('robustscaler', RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True,
    45. 

  45.        with_scaling=True)), ('elasticnet', ElasticNet(alpha=0.0002, copy_X=True, fit_intercept=True, l1_ratio=0.9,
    46. 

  46.       max_iter=1000, normalize=False, positive=False, precompute=False,
    47. 

  47.       random_state=101010, selection='cyclic', tol=0.0001,
    48. 

  48.       warm_start=False))])
    49. 

  49. GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
    50. 

  50.              learning_rate=0.02, loss='huber', max_depth=3,
    51. 

  51.              max_features='sqrt', max_leaf_nodes=None,
    52. 

  52.              min_impurity_decrease=0.0, min_impurity_split=None,
    53. 

  53.              min_samples_leaf=15, min_samples_split=10,
    54. 

  54.              min_weight_fraction_leaf=0.0, n_estimators=3000,
    55. 

  55.              presort='auto', random_state=101010, subsample=1.0, verbose=0,
    56. 

  56.              warm_start=False)
    57. 

  57. Pipeline(memory=None,
    58. 

  58.      steps=[('robustscaler', RobustScaler(copy=True, quantile_range=(25.0, 75.0), with_centering=True,
    59. 

  59.        with_scaling=True)), ('lasso', Lasso(alpha=0.0005, copy_X=True, fit_intercept=True, max_iter=1000,
    60. 

  60.    normalize=False, positive=False, precompute=False, random_state=101010,
    61. 

  61.    selection='cyclic', tol=0.0001, warm_start=False))])
    62. 

  62. AVERAGED MODELS
    63. 

  63. 

  64. RMSLE: 0.1066 (+/- 0.0068)
    65. 

  65.