output.txt 1.7 KB

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  1. Total Percent
  2. PoolQC 1453 0.995205
  3. MiscFeature 1406 0.963014
  4. Alley 1369 0.937671
  5. Fence 1179 0.807534
  6. FireplaceQu 690 0.472603
  7. LotFrontage 259 0.177397
  8. GarageCond 81 0.055479
  9. GarageType 81 0.055479
  10. GarageYrBlt 81 0.055479
  11. GarageFinish 81 0.055479
  12. GarageQual 81 0.055479
  13. BsmtExposure 38 0.026027
  14. BsmtFinType2 38 0.026027
  15. BsmtFinType1 37 0.025342
  16. BsmtCond 37 0.025342
  17. BsmtQual 37 0.025342
  18. MasVnrArea 8 0.005479
  19. MasVnrType 8 0.005479
  20. Electrical 1 0.000685
  21. ElasticNetCV(alphas=[0.0001, 0.0005, 0.001, 0.01, 0.1, 1, 10], copy_X=True,
  22. cv=None, eps=0.001, fit_intercept=True,
  23. l1_ratio=[0.01, 0.1, 0.5, 0.9, 0.99], max_iter=5000, n_alphas=100,
  24. n_jobs=1, normalize=False, positive=False, precompute='auto',
  25. random_state=None, selection='cyclic', tol=0.0001, verbose=0)
  26. R2: 0.9009282836396285
  27. RMSE: 0.11921419830906911
  28. RMSLE: 0.00919779987990656
  29. Test
  30. R2: 0.8967299617537993
  31. RMSE: 0.11097042355484425
  32. RMSLE: 0.008597195869364641
  33. Accuracy: 0.88 (+/- 0.10)
  34. GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
  35. learning_rate=0.05, loss='huber', max_depth=3,
  36. max_features='sqrt', max_leaf_nodes=None,
  37. min_impurity_decrease=0.0, min_impurity_split=None,
  38. min_samples_leaf=15, min_samples_split=10,
  39. min_weight_fraction_leaf=0.0, n_estimators=3000,
  40. presort='auto', random_state=None, subsample=1.0, verbose=0,
  41. warm_start=False)
  42. R2: 0.9621967175050222
  43. RMSE: 0.07561214754146357
  44. RMSLE: 0.005962683820457251
  45. Test
  46. R2: 0.9020742193997819
  47. RMSE: 0.10748560955595318
  48. RMSLE: 0.008197530387461648
  49. Accuracy: 0.89 (+/- 0.04)