AI带你省钱旅游!精准预测民宿房源价格!( 八 )

运行结果如下
Fitting 5 folds for each of 15 candidates, totalling 75 fits[CV 1/5] END ..................max_depth=80, n_estimators=50; total time=2.4s[CV 2/5] END ..................max_depth=80, n_estimators=50; total time=1.9s[CV 3/5] END ..................max_depth=80, n_estimators=50; total time=1.9s[CV 4/5] END ..................max_depth=80, n_estimators=50; total time=1.9s[CV 5/5] END ..................max_depth=80, n_estimators=50; total time=1.9s[CV 1/5] END .................max_depth=80, n_estimators=100; total time=3.8s[CV 2/5] END .................max_depth=80, n_estimators=100; total time=3.8s[CV 3/5] END .................max_depth=80, n_estimators=100; total time=3.9s[CV 4/5] END .................max_depth=80, n_estimators=100; total time=3.8s[CV 5/5] END .................max_depth=80, n_estimators=100; total time=3.8s[CV 1/5] END .................max_depth=80, n_estimators=200; total time=7.5s[CV 2/5] END .................max_depth=80, n_estimators=200; total time=7.7s[CV 3/5] END .................max_depth=80, n_estimators=200; total time=7.7s[CV 4/5] END .................max_depth=80, n_estimators=200; total time=7.6s[CV 5/5] END .................max_depth=80, n_estimators=200; total time=7.6s[CV 1/5] END .................max_depth=80, n_estimators=300; total time=11.3s[CV 2/5] END .................max_depth=80, n_estimators=300; total time=11.4s[CV 3/5] END .................max_depth=80, n_estimators=300; total time=11.7s[CV 4/5] END .................max_depth=80, n_estimators=300; total time=11.4s[CV 5/5] END .................max_depth=80, n_estimators=300; total time=11.4s[CV 1/5] END .................max_depth=80, n_estimators=400; total time=15.1s[CV 2/5] END .................max_depth=80, n_estimators=400; total time=16.4s[CV 3/5] END .................max_depth=80, n_estimators=400; total time=15.6s[CV 4/5] END .................max_depth=80, n_estimators=400; total time=15.2s[CV 5/5] END .................max_depth=80, n_estimators=400; total time=15.6s[CV 1/5] END ..................max_depth=90, n_estimators=50; total time=1.9s[CV 2/5] END ..................max_depth=90, n_estimators=50; total time=1.9s[CV 3/5] END ..................max_depth=90, n_estimators=50; total time=2.0s[CV 4/5] END ..................max_depth=90, n_estimators=50; total time=2.0s[CV 5/5] END ..................max_depth=90, n_estimators=50; total time=2.0s[CV 1/5] END .................max_depth=90, n_estimators=100; total time=3.9s[CV 2/5] END .................max_depth=90, n_estimators=100; total time=3.9s[CV 3/5] END .................max_depth=90, n_estimators=100; total time=4.0s[CV 4/5] END .................max_depth=90, n_estimators=100; total time=3.9s[CV 5/5] END .................max_depth=90, n_estimators=100; total time=3.9s[CV 1/5] END .................max_depth=90, n_estimators=200; total time=8.7s[CV 2/5] END .................max_depth=90, n_estimators=200; total time=8.1s[CV 3/5] END .................max_depth=90, n_estimators=200; total time=8.1s[CV 4/5] END .................max_depth=90, n_estimators=200; total time=7.7s[CV 5/5] END .................max_depth=90, n_estimators=200; total time=8.0s[CV 1/5] END .................max_depth=90, n_estimators=300; total time=11.6s[CV 2/5] END .................max_depth=90, n_estimators=300; total time=11.8s[CV 3/5] END .................max_depth=90, n_estimators=300; total time=12.2s[CV 4/5] END .................max_depth=90, n_estimators=300; total time=12.0s[CV 5/5] END .................max_depth=90, n_estimators=300; total time=13.2s[CV 1/5] END .................max_depth=90, n_estimators=400; total time=15.6s[CV 2/5] END .................max_depth=90, n_estimators=400; total time=15.9s[CV 3/5] END .................max_depth=90, n_estimators=400; total time=16.1s[CV 4/5] END .................max_depth=90, n_estimators=400; total time=15.7s[CV 5/5] END .................max_depth=90, n_estimators=400; total time=15.8s[CV 1/5] END .................max_depth=100, n_estimators=50; total time=1.9s[CV 2/5] END .................max_depth=100, n_estimators=50; total time=2.0s[CV 3/5] END .................max_depth=100, n_estimators=50; total time=2.0s[CV 4/5] END .................max_depth=100, n_estimators=50; total time=2.0s[CV 5/5] END .................max_depth=100, n_estimators=50; total time=2.0s[CV 1/5] END ................max_depth=100, n_estimators=100; total time=4.0s[CV 2/5] END ................max_depth=100, n_estimators=100; total time=4.0s[CV 3/5] END ................max_depth=100, n_estimators=100; total time=4.1s[CV 4/5] END ................max_depth=100, n_estimators=100; total time=4.0s[CV 5/5] END ................max_depth=100, n_estimators=100; total time=4.0s[CV 1/5] END ................max_depth=100, n_estimators=200; total time=7.8s[CV 2/5] END ................max_depth=100, n_estimators=200; total time=7.9s[CV 3/5] END ................max_depth=100, n_estimators=200; total time=8.1s[CV 4/5] END ................max_depth=100, n_estimators=200; total time=7.9s[CV 5/5] END ................max_depth=100, n_estimators=200; total time=7.8s[CV 1/5] END ................max_depth=100, n_estimators=300; total time=11.8s[CV 2/5] END ................max_depth=100, n_estimators=300; total time=12.0s[CV 3/5] END ................max_depth=100, n_estimators=300; total time=12.8s[CV 4/5] END ................max_depth=100, n_estimators=300; total time=11.4s[CV 5/5] END ................max_depth=100, n_estimators=300; total time=11.5s[CV 1/5] END ................max_depth=100, n_estimators=400; total time=15.1s[CV 2/5] END ................max_depth=100, n_estimators=400; total time=15.3s[CV 3/5] END ................max_depth=100, n_estimators=400; total time=15.6s[CV 4/5] END ................max_depth=100, n_estimators=400; total time=15.3s[CV 5/5] END ................max_depth=100, n_estimators=400; total time=15.3s

经验总结扩展阅读