output.txt 2.7 KB

1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677
  1. Total Percent
  2. PoolQC 2909 0.996574
  3. MiscFeature 2814 0.964029
  4. Alley 2721 0.932169
  5. Fence 2348 0.804385
  6. FireplaceQu 1420 0.486468
  7. LotFrontage 486 0.166495
  8. GarageCond 159 0.054471
  9. GarageQual 159 0.054471
  10. GarageYrBlt 159 0.054471
  11. GarageFinish 159 0.054471
  12. GarageType 157 0.053786
  13. BsmtCond 82 0.028092
  14. BsmtExposure 82 0.028092
  15. BsmtQual 81 0.027749
  16. BsmtFinType2 80 0.027407
  17. BsmtFinType1 79 0.027064
  18. MasVnrType 24 0.008222
  19. MasVnrArea 23 0.007879
  20. MSZoning 4 0.001370
  21. BsmtHalfBath 2 0.000685
  22. Utilities 2 0.000685
  23. Functional 2 0.000685
  24. BsmtFullBath 2 0.000685
  25. BsmtFinSF1 1 0.000343
  26. Exterior1st 1 0.000343
  27. Exterior2nd 1 0.000343
  28. BsmtFinSF2 1 0.000343
  29. BsmtUnfSF 1 0.000343
  30. TotalBsmtSF 1 0.000343
  31. SaleType 1 0.000343
  32. Electrical 1 0.000343
  33. KitchenQual 1 0.000343
  34. GarageArea 1 0.000343
  35. GarageCars 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: 0.8877978792415331
  42. RMSE: 0.1261931003284261
  43. RMSLE: 0.009699574112464507
  44. Test
  45. R2: 0.887363773943292
  46. RMSE: 0.11624163450366806
  47. RMSLE: 0.008977388659798451
  48. Accuracy: 0.87 (+/- 0.10)
  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: 0.961533307797864
  58. RMSE: 0.07603933958854966
  59. RMSLE: 0.005927534479527882
  60. Test
  61. R2: 0.8980531905767903
  62. RMSE: 0.11206606269257069
  63. RMSLE: 0.00866279547460908
  64. Accuracy: 0.89 (+/- 0.04)
  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: 0.9966543314229578
  71. RMSE: 0.02307409259418883
  72. RMSLE: 0.0017734224025037196
  73. Test
  74. R2: 0.908260191062412
  75. RMSE: 0.10927938903982129
  76. RMSLE: 0.008397817491435899
  77. Accuracy: 0.89 (+/- 0.04)