ML之回归预测:利用Lasso、ElasticNet、GBDT等算法构建集成学习算法AvgModelsR对国内某平台上海2020年6月份房价数据集【12+1】进行回归预测(模型评估、模型推理)
目录
利用Lasso、ElasticNet、GBDT等算法构建集成学习算法AvgModelsR对国内某平台上海2020年6月份房价数据集【12+1】进行回归预测(模型评估、模型推理)
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ML之回归预测:利用Lasso、ElasticNet、GBDT等算法构建集成学习算法AvgModelsR对国内某平台上海2020年6月份房价数据集【12+1】进行回归预测(模型评估、模型推理)
ML之回归预测:利用Lasso、ElasticNet、GBDT等算法构建集成学习算法AvgModelsR对国内某平台上海2020年6月份房价数据集【12+1】进行回归预测(模型评估、模型推理)实现
- (3000, 13) 13 3000
-
- total_price object
- unit_price object
- roomtype object
- height object
- direction object
- decorate object
- area object
- age float64
- garden object
- district object
- total_price_Num float64
- unit_price_Num int64
- area_Num float64
- dtype: object
-
- Index(['total_price', 'unit_price', 'roomtype', 'height', 'direction',
- 'decorate', 'area', 'age', 'garden', 'district', 'total_price_Num',
- 'unit_price_Num', 'area_Num'],
- dtype='object')
-
- total_price unit_price roomtype ... total_price_Num unit_price_Num area_Num
- 0 290万 46186元/平米 2室1厅 ... 290.0 46186 62.79
- 1 599万 76924元/平米 2室1厅 ... 599.0 76924 77.87
- 2 420万 51458元/平米 2室1厅 ... 420.0 51458 81.62
- 3 269.9万 34831元/平米 2室2厅 ... 269.9 34831 77.49
- 4 383万 79051元/平米 1室1厅 ... 383.0 79051 48.45
-
- [5 rows x 13 columns]
-
- total_price unit_price roomtype ... total_price_Num unit_price_Num area_Num
- 2995 230万 43144元/平米 1室1厅 ... 230.0 43144 53.31
- 2996 372万 75016元/平米 1室1厅 ... 372.0 75016 49.59
- 2997 366万 49973元/平米 2室1厅 ... 366.0 49973 73.24
- 2998 365万 69103元/平米 2室1厅 ... 365.0 69103 52.82
- 2999 420万 49412元/平米 2室2厅 ... 420.0 49412 85.00
-
- [5 rows x 13 columns]
- <class 'pandas.core.frame.DataFrame'>
- RangeIndex: 3000 entries, 0 to 2999
- Data columns (total 13 columns):
- Column Non-Null Count Dtype
- --- ------ -------------- -----
- 0 total_price 3000 non-null object
- 1 unit_price 3000 non-null object
- 2 roomtype 3000 non-null object
- 3 height 3000 non-null object
- 4 direction 3000 non-null object
- 5 decorate 3000 non-null object
- 6 area 3000 non-null object
- 7 age 2888 non-null float64
- 8 garden 3000 non-null object
- 9 district 3000 non-null object
- 10 total_price_Num 3000 non-null float64
- 11 unit_price_Num 3000 non-null int64
- 12 area_Num 3000 non-null float64
- dtypes: float64(3), int64(1), object(9)
- memory usage: 304.8+ KB
-
- age total_price_Num unit_price_Num area_Num
- count 2888.000000 3000.000000 3000.000000 3000.000000
- mean 2001.453601 631.953450 58939.028333 102.180667
- std 9.112425 631.308855 25867.208297 62.211662
- min 1911.000000 90.000000 11443.000000 17.050000
- 25% 1996.000000 300.000000 40267.500000 67.285000
- 50% 2003.000000 437.000000 54946.000000 89.230000
- 75% 2008.000000 738.000000 73681.250000 119.035000
- max 2018.000000 9800.000000 250813.000000 801.140000
- AvgModelsR(models=(Pipeline(steps=[('robustscaler', RobustScaler()),
- ('lasso',
- Lasso(alpha=0.001, random_state=1))]),
- Pipeline(steps=[('robustscaler', RobustScaler()),
- ('elasticnet',
- ElasticNet(alpha=0.001, l1_ratio=0.9,
- random_state=3))]),
- GradientBoostingRegressor(random_state=5)))
- R2_res [0.9944881811696309, 0.000626615309319283, array([0.99470591, 0.99512495, 0.99435729, 0.99491104, 0.99334171])]
- MAE_res [-0.004994183753322101, 0.0001083601234287803, array([-0.00493338, -0.005202 , -0.00489054, -0.00498097, -0.00496404])]
- RMSE_res [-8.323227156546791e-05, 9.870911328329942e-06, array([-8.14778066e-05, -7.79621763e-05, -7.93078692e-05, -7.49049128e-05,
- -1.02508593e-04])]
- AvgModelsR(models=(Pipeline(steps=[('robustscaler', RobustScaler()),
- ('lasso',
- Lasso(alpha=0.001, random_state=1))]),
- Pipeline(steps=[('robustscaler', RobustScaler()),
- ('elasticnet',
- ElasticNet(alpha=0.001, l1_ratio=0.9,
- random_state=3))]),
- GradientBoostingRegressor(random_state=5)))
- Avg_Best_models Score value: 0.9947618159336031
- Avg_Best_models R2 value: 0.9947618159336031
- Avg_Best_models MAE value: 0.0064209273962331555
- Avg_Best_models MSE value: 9.023779248949011e-05
-
- Avg_Best_models模型花费时间: 0:06:14.344069
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