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一.模型与特征哪个重要? 参与Sberbank Russian Housing Market比赛,一开始使用sklearn的岭回归函数Ridge(),残差值一直是0.37左右,然后同样的特征又使用了XGboost,残差值降到了0.34左右,提高的还是挺显著的. 但是另外一些参赛选手,使用XGboost,残差值可以降到0.31左右.所以由此来看,xgboost模型确实厉害,效果很好. 通过比赛不断调整特征发现,特征多了未必好,少了重要特征更不行,只有留下最重要的特征,模型的残差值才会降到最低.所以…
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